Information diffusion prediction is crucial for understanding social network dynamics, yet existing methods often neglect user participation uncertainty. This oversight typically stems from an implicit participation homogeneity assumption, which treats all observed interactions as equally reliable propagation signals, leading to fragile inferred topologies and uncertainty contamination. To address this, we propose SIEVE, a novel framework employing two synergistic strategies. First, robust node representations are learned via controllable uncertainty injection coupled with associated contrastive learning, mitigating topological fragility. Second, an uncertainty-aware directed graph aggregation mechanism is introduced, which dynamically constructs asymmetric aggregation topologies with adaptive weighting, thereby suppressing uncertainty contamination. Experiments on four public datasets demonstrate that SIEVE significantly outperforms state-of-the-art methods, offering valuable insights for designing robust information diffusion prediction models.

As people benefit from the convenience of social networks, they are also exposed to security risks from malicious information. This article proposes a malicious and anti-malicious information propagation dynamics model based on higher-order diffusion networks to address these issues. Firstly, to tackle the problem of individuals' behavior being swayed by the emotional content of malicious information during propagation, we have established an emotional influence mechanism. At the same time, adding user preferences and external drivers proposes a way to calculate individual influence. Secondly, in response to the discrepancies in the propagation efficiency of malicious information among varying users, we establish a higher-order diffusion network and quantify these differences through the stratification of users into three distinct layers of nodes. Additionally, considering the implicit relationship among potential user groups, we establish a group interaction mechanism by mining group attributes and refine the structure of the propagation network. Lastly, given the coexistence and opposition between malicious and anti-malicious information, dynamic game theory is applied to define a state transition equation incorporating anti-malicious information propagators. Consequently, we propose the SAIR model, a novel paradigm for understanding the propagation of malicious and anti-malicious information using higher-order diffusion networks.

In Intelligent Transportation Systems (ITS), the accuracy of compensating for missing traffic data is critical. This directly impacts the effectiveness of traffic flow prediction and road condition monitoring. Inspired by image restoration techniques, this study introduces the Generative Adversarial Network (GAN) to enhance traffic data compensation. Firstly, to address the problem of converting traffic data into the traffic flow matrix of the road network, we propose the RoadNetIMatrix algorithm to generate the traffic flow matrix of the road network. This algorithm precisely captures traffic flow dynamics in road networks and provides a holistic representation of traffic states. Secondly, given the inherent spatio-temporal correlation in traffic data, we proposed a spatio-temporal collaborative mining component (STSSM). This component integrates the hidden temporal dependencies and spatial features of the mined traffic data into the GAN generator to improve the authenticity of the generated content and ensure the consistency of data compensation. Finally, addressing the influence of external characteristics of traffic data on data compensation results, an external information module based on a multi-head attention mechanism is constructed, which can effectively mine the influence of external factors of traffic data. Furthermore, spatio-temporal and external features are fused to further improve the accuracy of data compensation. Experiments show that the model has a higher accuracy of data compensation and a better generalization of the system in the case of multiple types or a high data loss rate.

As a fundamental task in understanding the information diffusion process, information diffusion prediction has garnered significant attention in recent years. However, most existing studies tend to focus on the structural characteristics of information diffusion while neglecting an important phenomenon: the propagation of topics often carries multiple intentions, which align with specific user cognition at different evolutionary stages. This diversity in propagation intentions and the dynamic nature of user cognition pose challenges for prediction tasks. To address the above issues, this article introduces Buzz, an information diffusion prediction model, by innovatively approaching the problem from the perspective of aligning multiple propagation intentions with dynamic user cognition. First, to tackle the dynamic hierarchy of propagation intentions, a concise and efficient cascade intention extraction module is designed. This module uses observed diffusion cascades as intention anchors and employs an improved self-attention mechanism to generate representations of the current multiple propagation intentions. Based on this, attention weights are utilized to dynamically stratify the hierarchical structure of propagation intentions. Second, to address the dynamic nature of user cognition, we take social relationships as cognition anchors to initialize the latent diffusion network and dynamically weight the adjacency matrix through temporal slicing. This accurately models the dynamic diffusion process of topics. On this foundation, the cascades are segmented along the diffusion timeline into corresponding time slices, and graph convolution is applied to refine user dynamic cognition. Finally, considering the complexity of aligning propagation intentions with user cognition, we design a multihead attention fusion module. This module aligns propagation intentions based on user cognition, enabling more precise selection of target users. The great performance on four public datasets validates the effectiveness of our proposed approach.

In contemporary society, online public opinion exerts significant influence, and guiding its trajectory is essential for maintaining social stability. To capture the interaction and evolution of competing guiding information during topic dissemination, a competitive guidance topic propagation model based on evolutionary game theory is developed. First, to address the interactive influence of multiple topic features, a multifactor analysis framework is established. Based on positive and negative sentiment, a metric for topic controversy is designed. Considering the effectiveness of multiple regression in measuring diverse features, multiple regression is introduced to evaluate the driving forces of different topic characteristics. This approach allows a detailed analysis of how various features affect both the dissemination impact and the dissemination potential. Second, to address the competition and cooperation among different types of guidance information, evolutionary game theory is introduced to design a user participation-driven game mechanism. The mechanism is used to analyze the competitive and cooperative dynamics between diverse guidance messages. It reveals the strategic landscape of positive and negative information dissemination and provides a theoretical foundation for modeling user state transitions. Finally, to address the dynamic nature of user state changes and considering the similarity between infectious disease transmission and topic propagation, a suspicion–hesitation–positive and negative information–immunity model is proposed. The model allows fine-grained modeling of user state transitions and provides effective theoretical support for public opinion management. Experimental results demonstrate that the proposed model effectively fits the propagation trends of guiding topics on the given dataset and captures competitive and cooperative relationships among different types of guiding information. Compared with baseline models, performance improves by at least 2.8%, providing a solution for modeling and analyzing the propagation process of guiding topics.

Guided topic detection plays a crucial role in public opinion management and crisis response. To address the adversarial nature of group stances and the dynamic evolution of topics, this study proposes a guided topic detection model based on topic evolution and group stances. First, considering the hidden nature of groups in guided topics, the TA-Louvain hidden community mining method is proposed to quickly and accurately discover the community structure and track the structural changes of the same group. Then, for the complexity of group features in guided topics, an attention mechanism-based group feature representation method AG2vec was designed, which can improve the accuracy and robustness of group feature representation. Finally, to address the confrontational nature of group stances, stance-related features are extracted by combining game theory with a multiple linear regression algorithm. Based on this, group change GRU (GC-GRU), a GRU-based guided topic detection model, is proposed to capture the dynamic evolution of group features over time. Experimental validation shows that the method can flexibly respond to the rapid evolution of topics. It also fully accounts for the confrontational and dynamic types of user group stances, thereby enabling the effective detection of guided topics.

In the topic dissemination space, various types of messages interact and collectively influence the propagation trends of derivative topics. To address this, a prediction model for group behavior in hotspot topics is proposed based on multitype complex messages. First, considering the complexity and high-dimensional nature of data in the topic feature space, sparse representation is employed to extract key user attributes in the derivative topic space and to calculate sparse vectors. Based on these sparse vectors, a node feature prediction submodel is subsequently constructed using regression algorithms. Second, considering the cooperative and competitive relationships between different message types, evolutionary game theory is applied to transform the dynamic interactions between messages into psychological games among users. By reflecting the optimal benefits of user strategies, it captures the driving forces of derivative messages and establishes a dynamic topic dissemination network. Additionally, a graph convolutional network is employed to extract user structural features, constructing a structure attribute prediction submodel. Finally, to overcome the limitations of individual submodels, the node feature and structure attribute prediction submodels are integrated to form a group behavior prediction model based on multitype message interactions. The input data is discretized using time slicing to enhance the model’s generalization ability. Experiments show that the proposed model accurately captures the complex interactions among multitype messages in the derivative topic space and effectively predicts group behavior and hotspot topic propagation trends.

This article investigates an emerging social commerce model that integrates social networks and e-commerce, highlighting the pivotal role of topic marketing in influencing consumers’ purchasing decisions. Within the framework of user-generated content (UGC), and accounting for the complex interplay among factors influencing topic propagation as well as the cooperative and competitive dynamics between topics, this study proposes a marketing topic propagation model based on factor analysis and the Stackelberg game. First, factor analysis was employed to extract two primary factors influencing topic propagation—user behavior and external environmental factors—thereby reducing the dimensionality of the variables. A multiple linear regression model was subsequently applied to quantify the contribution of these factors, establishing a robust quantitative foundation for optimizing marketing strategies. Second, to account for the competitive and cooperative interactions among topics, the behavior of ordinary users and influencers was analyzed using a Stackelberg game framework. This analysis reveals the complex interdependencies between cooperative and competitive dynamics in topic propagation. Finally, to address the behavioral variability and time-varying nature of users’ active participation or neglect (of topics), the traditional susceptible-infectious-recovered (SIR) model was refined by subdividing the infectious state into “marketing” and “neglected” states, resulting in the susceptible-marketing-neglected-recovered (SMNR) dynamic propagation model. By incorporating a temporal dimension, the model captures dynamic changes in states and parameters during topic propagation, thereby enhancing the accuracy of predictions regarding propagation paths and trends. Experimental results demonstrate that the proposed model effectively measures dynamic changes in influencing factors, incorporates game-theoretic relationships, and accurately predicts the temporal evolution of marketing topics. Its dynamic characteristics provide a robust theoretical foundation for optimizing social commerce marketing strategies and offer precise guidance for data-driven marketing decisions.

Due to comprehensive considerations regarding demand and cost control in traffic management, the performance indicators for checkpoint sensors can differ depending on the section of road. Consequently, some traffic data collected at checkpoints may be incomplete, which complicates data analysis. To address this issue, we propose using a neural network based on embedding synchronized spatio-temporal map data. Firstly, we designed a trajectory vectorization algorithm using word embedding techniques, modeling the road network with vehicle trajectories to capture spatial correlations among road checkpoints. Secondly, we constructed a spatio-temporal synchronization graph recovery neural network (STSGRN) that uses graph convolutional networks (GCNs) and gated recurrent units (GRUs) to account for the spatio-temporal characteristics of traffic data and fill in missing data across multiple dimensions. Finally, we developed an attention mechanism module for the spatio-temporal graph to extract dynamic dependencies at the spatio-temporal level. This architecture enhances the flexibility of the STSGRN in processing complex data with missing values. Experimental results demonstrate that the model effectively identifies spatial and temporal correlations in traffic data, enabling the accurate imputation of missing values. Compared to existing methods, it demonstrates significant improvements and superior generalization performance.

Aiming to optimize marketing promotion, an information dissemination model integrating trust and user preference is developed. The objective is to capture users’ behavioral mechanisms and enhance marketing decision-making efficiency. To measure users’ trust in key opinion leaders, an Interval Type-2 Fuzzy Sets (IT2FSs) -based trust evaluation model is created, enabling effective trust assessment and stimulating purchasing behavior. Regarding the dynamic nature of user preferences, a Hidden Markov Model (HMM) -based prediction algorithm is proposed to track interest changes and forecast repurchase behavior. Considering rational and irrational user behaviors in marketing information dissemination, two new states, repurchase state P and hesitant purchasing state H are introduced based on the Susceptible-Infected-Recovered (SIR) model. Then, the SIRPH social platform information dissemination model is constructed, achieving accurate prediction and enhancement of marketing information dissemination. Experimental results indicate that the SIRPH model reduces the peak purchasing users by 15–25%, extends topic lifecycles by 20–30%, and improves information spreading accuracy, demonstrating the effectiveness of trust and preference integration.

Recommender systems enhance user experience by delivering personalized suggestions derived from users' historical behavior. However, conventional approaches face challenges in processing large-scale data while simultaneously preserving user privacy and maintaining stable training. To address these issues, we propose Fed-MWAE, a novel federated variational autoencoder (VAE) framework for recommendation tasks. The framework incorporates a sparsely activated Mixture-of-Experts (MoE) module to model diverse user behavior patterns across expert subnetworks. A top-$k$gating mechanism selectively aggregates expert outputs, thereby improving computational efficiency without compromising accuracy. Furthermore, Fed-MWAE employs VAEs to capture complex latent structures and replaces the conventional Kullback-Leibler (KL) divergence with the Wasserstein distance, enabling smoother optimization and more stable convergence. Training is conducted in a federated learning setting, where local clients perform on-device updates to safeguard data privacy, and the updates are aggregated using the Federated Averaging (FedAvg) algorithm to enhance scalability and communication efficiency. Extensive experiments on four public datasets demonstrate that Fed-MWAE consistently outperforms strong baselines, achieving improvements of 5.46% in NDCG, 0.66% in Recall@20, 4.85% in Recall@50, and a 2.99% reduction in loss. These results validate the effectiveness of Fed-MWAE in balancing accuracy, efficiency, stability, and privacy in federated recommender systems.

Compared to traditional media, the open and instantaneous nature of social media makes it easier for rumors to emerge and spread, thereby causing more severe societal impacts. Therefore, identifying potential user connections within rumor-related topics holds critical significance for public opinion monitoring and social stability. Firstly, according to the richness and multi-level nature of user characteristics, the interests and preferences and social attributes of users are comprehensively analyzed, so as to realize the multi-dimensional and in-depth representation of user characteristics. Secondly, to better capture the complexity of rumor-related social interactions, a rumor user interaction node2vec method that leverages user interaction behavior is proposed. The method uses a biased random walk strategy that combines rumor influence and user interaction evolution. It effectively captures key attributes of the rumor interaction space, leading to a more precise representation of the network structure in rumor propagation. Finally, in view of the complexity of the relationship between different nodes, the graph attention network is used to effectively capture the interaction between nodes, and a social network link prediction method for rumor propagation is proposed. Experiments and analyses are conducted on multiple public datasets. The results demonstrate that the proposed link prediction model can accurately predict whether links will form between users, showing significant advantages in the specific and complex scenario of rumor propagation.

Client selection can result in more homogeneous data and improve the generalization performance of the global model. In the actual training environment, the heterogeneity of client devices and the non-IID characteristics of data seriously reduce the performance of the model. Therefore, this paper proposes a federated learning cluster selection model to alleviate client drift. To address the issue of unstable computing and communication resources among participants, we propose an algorithm that adaptively adjusts the task load based on the linear upper confidence bound algorithm (LinUCB). The algorithm mines the probability distribution of resources from historical training data,whilch helps alleviate the problem of dropout caused by device overload. To solve the high computational complexity problem caused by high-dimensional gradients, a method for extracting the updated direction of a local model is proposed based on updated weights. The updated direction is jointly represented by the class distribution calculated from the cross-entropy loss of the model on a balanced dataset, and the updated weights that are reduced in dimensionality. Finally, we propose a client clustering selection algorithm based on the updated direction to address the problem of client drift in collaborative training with heterogeneous data. The algorithm prioritizes selecting clients whose update directions are close to that of the global model, while simultaneously selecting clients with tilted update directions with equal preference. This makes the data distribution more homogeneous and alleviates the problem of client drift. Experimental results demonstrate that the proposed algorithm for adaptive task allocation can reduce the number of dropouts under resource-limited and unstable scenarios. The client selection algorithm shows good performance on three public datasets, MNIST, CIFAR-10 and AG_NEWS, outperforming six baseline methods across varying levels of data heterogeneity.Our code is public athttps://github.com/Wannery/MBUT-CS.

In the context of social commerce, user dissemination behavior is influenced by the coupling of multiple factors, such as the timeliness of marketing content and the dynamics of social relationships. Existing methods are limited in predictive accuracy due to neglecting the cold-start data sparsity and the evolution patterns of behavior sequences. To address this issue, we propose a predictive model for user dissemination behavior in social commerce, leveraging transfer learning and sparse representation techniques. Firstly, to tackle the problem of sparse cold-start topic data, a cross-domain transfer learning framework is designed. Historical topics are matched as the source domain based on semantic similarity, and knowledge transfer is achieved through a domain adaptation loss function, thereby improving the model's performance for cold-start or low-engagement topics. Second, to address the challenge of high-dimensional feature modeling, a dual sparse coding method is proposed. This method performs low-rank decomposition on both the user attribute matrix and the social adjacency matrix, preserving core structural information while reducing computational complexity. Finally, to handle the dynamic propagation characteristics, a utility function combining tripartite game theory is constructed, incorporating the marketing content attractiveness, user social influence, and time decay factor. This is embedded into the weight generation module of the relationship-aware graph attention network. By using a dynamic time encoder, the evolution pattern of behaviors is captured, enabling accurate prediction of social commerce user behavior. Experimental results show that this model can not only accurately predict users' interactions with marketing topics but also reveal the dissemination patterns of marketing content, providing strong support for optimizing marketing strategies. This research offers an effective technical means for the precise placement of marketing content and user behavior analysis in social commerce.

The significant societal impact of online public opinion has spurred extensive research into the underlying mechanisms of information diffusion. While existing diffusion prediction models have effectively leveraged network structure, they often lack a deep understanding of the intrinsic patterns governing information dissemination, thus limiting their predictive power. To address this critical gap, we introduce PMRCA, a novel information diffusion prediction model inspired by social psychology and driven by fundamental propagation patterns. PMRCA posits that two key patterns underpin topic propagation: multisource resonance, where consensus emerges from consistent narratives across multiple sources, and cognitive adaptation, where information is tailored to align with audience cognitive features. Correspondingly, PMRCA incorporates two dedicated learning tasks: structural contrastive learning based on node homogeneity to capture multisource resonance, and clustering contrastive learning based on cognitive clusters to model cognitive adaptation. Extensive experiments on four real-world datasets demonstrate that PMRCA significantly outperforms existing mainstream models. Importantly, our pattern-driven approach is model-agnostic, allowing it to be flexibly applied to various diffusion prediction scenarios and enhance performance.

Clustered Federated Learning (CFL) is a machine learning paradigm that balances local and global training to address limited local data and reduce the negative effects of data heterogeneity on the global model. However, data heterogeneity and insufficient samples in small clusters remain key challenges in CFL, and existing methods often overlook the potential of cross-cluster knowledge sharing, hindering further performance gains. To address these challenges, a clustered federated learning framework with knowledge sharing, termed KS-CFL (Knowledge-Sharing Clustered Federated Learning), is proposed. First, to tackle client data heterogeneity, local model gradients are used to extract representative features, and a data-driven heterogeneity metric is designed to identify client differences and guide cluster formation. Second, a divisive iterative clustering method is introduced to adjust cluster structures during training dynamically, improving client clustering precision. Finally, a weight-sharing mechanism inspired by multitask learning is introduced to mitigate data scarcity in small clusters, enhance model performance, and accelerate convergence. Experimental results show that KS-CFL outperforms state-of-the-art CFL methods on the CIFAR-10 and FEMNIST datasets. In the FEMNIST-D2 scenario, KS-CFL improves accuracy by 3.69% over FLT and reduces variance, enhancing fairness. On CIFAR-10-D1 and CIFAR-10-D2, it achieves accuracy gains of 2.18% and 2.66%, respectively, with lower variance. These results highlight the effectiveness of KS-CFL in heterogeneous data environments.

Accurate traffic speed prediction plays a key role in transportation planning. Inspired by advanced natural language processing (NLP) techniques in speech recognition, this paper proposes a bidirectional spatio-temporal gated graph convolutional network (BiSTGG) based on the attention mechanism for urban traffic speed prediction. The model analogizes the average daily traffic speed with word frequencies in speech, thus establishing a novel semantic perspective for traffic modeling. In order to comprehensively assess the similarity between traffic speed trajectories, the model introduces a dynamic temporal regularization (DTW) algorithm, which enables the construction of dynamic semantic similarity subgraphs that go beyond the traditional spatial proximity modeling approach. Meanwhile, the model applies the multi-head graph attention mechanism to ensure that the complex and diverse interactions in the road network can be captured effectively. In addition, the model introduces spatial identification vectors to enhance robustness and direct attention to focus on key features. The model also incorporates a bi-directional gated recurrent unit (Bi-GRU) structure with an attention mechanism to capture the complex forward and backward time dependencies in traffic patterns. Experiments on two large-scale real datasets show that BiSTGG performs well in terms of prediction accuracy and robustness, and significantly improves real-time traffic speed prediction.

The development of social networks has prompted a shift in marketing strategies, with a surging demand for marketing in vertical domains characterized by high user stickiness and specialization. To address this, we propose a traceability model based on domain preference and heterogeneous networks. Firstly, considering the problem of marketing topic vertical domains features metric and the influence of users' preference degree for domains on topic propagation, the domains are treated as latent semantics, and the user-topic association matrix sparse matrix is densified using a latent factor model to mine the domain preference information efficiently. Secondly, considering the complexity of the association between multi-type elements in marketing topics, the HLN2vec (Heterogeneous Layer- wise Networks) model is proposed. This model uses heterogeneous network representation learning and incorporates multi-layer attention networks to learn the representations to portray a marketing topic's key elements and their relationships. Finally, this paper proposes the DP-Rank(Domain Preference-based) algorithm, which uses domain preference features and an adaptive random walking strategy to quantify element influence. Based on experiments, the proposed model robustly applies in social networks and exhibits clear advantages in measuring vertical domain features of marketing topics, constructing multi-type element relationship networks, and discovering core element influence.

Vertical federated learning can aggregate participant data features. To address the issue of insufficient overlapping data in vertical federated learning, this study presents a generative adversarial network model that allows distributed data augmentation. First, this study proposes a distributed generative adversarial network FeCGAN for multiple participants with insufficient overlapping data, considering the fact that the generative adversarial network can generate simulation samples. This network is suitable for multiple data sources and can augment participants' local data. Second, to address the problem of learning divergence caused by different local distributions of multiple data sources, this study proposes the aggregation algorithm FedKL. It aggregates the feedback of the local discriminator to interact with the generator and learns the local data distribution more accurately. Finally, given the problem of data waste caused by the unavailability of nonoverlapping data, this study proposes a data augmentation method called VFeDA. It uses FeCGAN to generate pseudo features and expands more overlapping data, thereby improving the data use. Experiments showed that the proposed model is suitable for multiple data sources and can generate high-quality data.

Cross-domain recommendation can combine users' interests in multiple domains to achieve diversification of recommended content. To solve the problem of complex and changeable interest relationship among different domains, this paper proposes a cross-domain recommendation model integrating user's long-term and short-term interests. Firstly, to solve the problem of different data distributions in different domains, this paper proposes the graph convolutional network with maximum mean discrepancy (MMD), which gradually aligns the feature representation of shared potential space during training to reduce the influence of negative transfer. Secondly, to solve the problem of different features of items in different domains, considering the advantage of attention mechanism in distinguishing importance, a hierarchical attention mechanism focusing on item features is proposed to build users' short-term interests in source domain. Finally, aiming at the dynamic influence of users' long-term and short-term interest, a multi-type interest dynamic aggregation module based on gated network is proposed, which not only integrates users' preferences in different domains, but also takes into account the characteristics of items to be predicted. Experiments show that the model can effectively capture users' multi-type interests in source domain and target domain on real data set, so as to improve the recommendation accuracy of target domain.

The propagation of Internet rumors has complex dynamics. The traditional propagation model, based mainly on rumor and antirumor messages, provides a static analysis of the rumor propagation situation. This article introduces the concept of a prom-rumor (promoting rumor) message and thoroughly investigates competition and cooperation among multitype rumor messages. First, we employ a multiple linear regression method to construct the information influence mechanism by extracting network structure, topic content information, and historical behavior information, considering the complexity of spreading multitype compound rumor messages. Then, we introduce game theory and formulate a game strategy to capture the competition and cooperation among rumors, antirumors, and prom-rumors. Furthermore, we consider the interactivity in multitype compound rumor messages and combine the dynamic game to construct the driving mechanism of rumors, antirumors, and prom-rumors. Finally, we define the user-state transfer equation and propose a dynamic model of information propagation based on rumors, antirumors, prom-rumors, and the dynamic game while considering the multidimensionality and polymorphism of multitype compound rumor messages and combining mean-field theory and the infectious disease model. In validation experiments on real-life data, the model accurately captures the game propagation process of the multitype rumors and predicts the propagation trends in rumor networks.

The rapid spread of online rumors has significant negative impacts on the online ecosystem and social order, which is closely tied to users’ cognitive traits. To explore the mechanisms of rumor propagation driven by cognition and mitigate its harm, we propose an information diffusion model based on the rumor, antirumor, and cognitive game. First, to address the uncertainty and difficulty in quantifying cognitive biases, and considering the advantages of fuzzy logic theory in handling uncertainty, a fuzzy logic-based algorithm for measuring user cognitive biases is proposed. Moreover, recognizing the nonlinear relationships among various factors, polynomial functions are introduced as the output of fuzzy rules to more accurately describe these complex relationships. Second, regarding the symbiotic and antagonistic relationships among multiple types of rumor information under the influence of cognitive biases, and leveraging the strengths of game theory in analyzing complex systems characterized by coexistence of symbiosis and antagonism, an evolutionary game-based rumor-antirumor user behavior mechanism is developed. This provides a robust theoretical foundation for understanding user state transitions and their evolutionary patterns. Finally, integrating the aforementioned research, and considering that the dynamic evolution of user cognition leads to variations in trust responses and attitudes toward rumor and antirumor information, the concepts of trust states—trust in rumor (TR) and trust in antirumor (TA)—are introduced into the classical susceptible-infectious-recovered (SIR) model. On this basis, a rumor propagation susceptible-trused-infectious-recovered (STIR) model incorporating user cognitive biases and evolutionary game theory is further constructed. Experimental results demonstrate that this model effectively reveals the game dynamics of multiple types of rumor information, providing a more efficient framework for studying rumor propagation in social networks.

As topics evolve during the dissemination process, their derivative characteristics play an important role in revealing the mechanism of topic dissemination in social networks. Due to users’ cognitive inertia, followers of antecedent topics tend to pay more attention to derivative topics than ordinary users, and this cognitive inertia is directly related to the correlation between these two topics. Based on this finding, we propose a derivation topic dissemination model based on iterative multitopic derivation and social psychology, taking into full consideration users’ emotional accumulation of antecedent topics and repeated derivation of topics. First, a multiple linear regression model is used to construct a metric algorithm for user antecedent sentiment and to effectively analyze the dynamics of antecedent sentiment accumulation affecting the spread of derivative topics. Second, to analyze the interactions between and within multitopic layers in full, an iterative multitopic dissemination model iterative-susceptible infectious recovery (SIR) is proposed. In addition, the association degree is introduced to define the cross-model state transition equation by considering the association and differences among multitopics. Last, considering the influence of cognitive inertia and “continuous attention psychology” in social psychology, we construct a user psychology-based driving force model which can further improve the cross-model state transition equation. According to the experiments, the model can effectively reveal the influence of different factors on the dissemination trend of derivative topics in social networks, as well as depict the dissemination dynamics of derivative topics.

In topic dissemination, accurately predicting the behavior of derived topic dissemination models enables the implementation of timely measures to effectively mitigate negative impacts. Addressing the issues of data scarcity and the influence of leaders on users, this article proposes a guided derived topic propagation model based on topic identity and transfer learning. To tackle the sparsity of early data on derived topics, we introduce generative adversarial networks (GANs) to enhance data integrity and continuity, given their efficacy in compensating for missing data. Additionally, recognizing the potential for domain shifts during topic evolution, we incorporate transfer learning to facilitate domain adaptation and improve the accuracy of the derived topic propagation model. To address the significant impact of leader authority on user perspectives, we incorporate the social psychological concept of “identity theory” and design a metric for topic identity based on this theory to quantify the leader’s influence on user viewpoints. Concurrently, considering user identification with topics, we propose a metric for topic domain co-presence, incorporating interest preferences to more accurately represent user engagement with the topic. Finally, to account for the dynamic nature of user viewpoints, we employ graph convolutional networks (GCNs) to model user perspectives over time. The use of GCNs, based on temporal slices, effectively handles the interconnections between user nodes and explores the influence of interuser interactions on viewpoints. Experimental results demonstrate that the proposed model not only enhances data integrity and continuity but also improves the prediction of the propagation dynamics of guided derived topics.

The propagation of hot topics often gives rise to a series of derivative topics. In view of the sparsity of user behavior data and the cognitive accumulation of the original topic, a prediction model of derived topic propagation based on cognitive accumulation and transfer learning is proposed. First, for the complexity of the derived topic feature space, considering the relation and difference between derivative topics and original topics, this study designs I(Iterative)T(Topic)2vec, a topic iterative representation method based on original topics to get the low-dimensional representation of the derived topic feature space more richly from the perspectives of both original topics and derivative topics. Second, it aims at the problem of users’ cognitive accumulation of the original topic before the outbreak of derivative topic. The subjective game theory is introduced to construct the cognitive influence of users. At the same time, considering the timeliness of the propagation cycle of derivative topics, we discretized the derivative topic data, and further proposed a derivative topic propagation model based on Subjective Adapt-CNN (SA-CNN). Finally, the sparsity of effective behavior data of users at the beginning of the outbreak of derivative topics is discussed. Considering the rich user behavior data in the communication history of the original topic, data migration is carried out by using the original topic. At the same time, the domain adaptive method based on Transfer Component Analysis (TCA) is introduced to achieve feature adaptation from the original topic data to the derived topic data, further improving the accuracy of the derived topic propagation model. Experiments show that this model can not only effectively alleviate the problem of data sparsity but also perceive the propagation situation of derived topics well.

In social networks, studying rumor propagation patterns is essential for curbing the spread of rumors. Given the coexistence and conflict of multiple-type rumor information, as well as users’ cognitive differences, this article presents a rumor propagation model grounded in user cognition and evolutionary game theory. First, considering the potential impact of social relationships between users on rumor propagation, the KD-Tree algorithm is employed to uncover hidden connections between users, thereby enriching the topology of the user’s social network. Second, a user behavior driving mechanism for rumor, anti-rumor, and motivation-rumor types is constructed based on evolutionary games to reflect the interactive and strategic nature of users’ responses. Moreover, the Lotka-Volterra equation is utilized to explore the dynamic game of multi-type rumor information and the cognitive process of users. Finally, to address differences in users’ cognition, this article introduces the anti-rumor trust state A and the motivation-rumor trust state M , which arise from users’ exposure to multiple types of rumor information. Based on these trust states, a rumor propagation model, SIAMR, is constructed using user cognition and evolutionary game theory. Experiments demonstrate that the model accurately captures the dynamic interactions between multi-type rumor information and the transmission process of rumor topics in social networks. The proposed model integrates cognitive psychology with a strategic interaction framework, offering a more realistic representation of rumor propagation behavior in the real world. Experimental results reveal that SIAMR improves prediction accuracy by 14.23% over baseline models in simulating the dynamics of multiple types of rumors, effectively capturing users’ cognitive influences and the mechanisms of information competition.

This study was inspired by video forgery detection techniques. If the topic space at a certain time is considered as a frame image, the consecutive frame images over time could be viewed as a video. Then the rumor topic detection problem is transformed into a topic video forgery detection problem. Thus, a novel rumor detection method was proposed. First, a Topic2RGB algorithm was proposed to convert comment users into pixel points. The algorithm views commenting users as pixel points while using game theory to mine user pro-opposition emotions as RGB information. Secondly, a Topic2Video algorithm was proposed to convert the topic space into video. The algorithm converts the topic space into frame images. Meanwhile, the topic space is time-sliced, then the topic space is transformed into a video. Finally, the volatility of user emotional confrontation during a long time in the topic space is like the change of characteristics of frame images in forgeries videos. Then, a topic video rumor detection method (TVRD) was proposed. The experiments indicate that the method successfully verifies the viability of the topic videolization for rumor detection. Additionally, the method also demonstrates the effectiveness of user emotion confrontation of topic space on detection performance.

Discovering hidden key users of leading topics plays an important role in opinion control and risk prevention. Aiming at the dynamic nature of key users’ intentions and other problems, a key user discovery model based on behavioral intentions and implicit relationships is proposed. First, to address the dynamic nature of key users’ intentions, the dynamic latent Dirichlet allocation method is introduced. This approach effectively mines topic evolution in text data, uncovering dynamic behavioral themes of key users and analyzing evolutionary relationships between topics. Meanwhile, incremental learning is introduced to quantify the dynamic behavioral intentions of key users more precisely. Second, a random wandering strategy based on user interaction degree and propagation depth is designed to address the hidden nature of user relationships. The strategy introduces the user interaction degree designed by the social cognition theory and the propagation depth designed by the propagation chain theory to better explore the hidden user interaction relationships. Finally, for the timeliness of key user identification, considering the advantage of dynamic evolution for real-time interaction, dynamic evolution is introduced to effectively analyze the dynamic structure of topic networks, and attention mechanism is introduced to improve the adaptivity of the model. The experiments show that this paper verifies the factuality of the existence of hidden key users dominating the promotion behind the guiding public opinion, and is more effective in tracing the hidden key users in the topics.

Federated learning is an important distributed machine learning paradigm. This study proposes a privacy-preserving data augmentation model for federated learning of heterogeneous data, which is able to mitigate heterogeneity and augmenting the participant’s local data while protecting data privacy. First, to address the problem of global model bias due to heterogeneous data, this study proposes a distributed generative adversarial network FedEqGAN. The model introduces a multi-source data feature fusion mechanism, which can learn the features of each data source to generate synthetic data. Second, addressing the privacy leakage issue caused by the disclosure of data distribution information, this paper proposes an encryption algorithm for heterogeneous environments FedHE, which utilizes homomorphic encryption to protect local data distributions and aggregates local data information through KL dispersion in order to construct global data distributions. Finally, for the privacy leakage problem caused by uploading model parameters in federation training, this paper proposes a federation model parameter encryption algorithm DPFedMP. This algorithm dynamically injects Gaussian noise into the model parameters according to the difference of data distribution to realize differential privacy protection and update the global model. Experiments show that the method is applicable to heterogeneous data environment, significantly enhancing model performance while ensuring data security.

Conventional unsupervised multi source domain adaptation (UMDA) methods are based on the assumption that all source domain data are accessible directly. Aiming at the problem that current UMDA methods cannot directly obtain source domain data in federated learning (FL), a knowledge distillation-based multisource domain adaptation method adapted to FL is proposed. First of all, considering that knowledge distillation allows learning solely through model access, this article adopts an improved voting mechanism by applying a smoothing technique to the confidence distribution in the source domain models. This reduces the influence of models with extreme high confidence, thereby extracting high-quality consensus knowledge. Second, this article designs a teacher model adaptive weighting strategy. It identifies irrelevant domains and malicious domains according to the similarity between consensus knowledge and the output of the teacher model. Then, it improves the robustness of the model for negative transfer. Finally, this article introduces the idea of contrastive learning. It can control the drift of a single source domain and bridge the deviation between the representation learned by the local model and the global model. Experiments show that the method proposed in this article is superior to the mainstream UMDA methods. Moreover, it is robust to negative transfer, which is suitable for many practical FL applications.

Rumors in different topic domains have different text characteristics but similar emotional tendencies. To resolve the scarce-data problem in some rumor-topic domains, this study proposes a cross-domain rumor-propagation model, which is based on transfer learning. First, given the diversity and complexity of the rumor-propagation landscape, this study introduces a novel method, User-Retweet-Rumor2vec (URR2vec), which leverages the power of representation learning to uncover latent features within rumor topics. It also displays the forwarding relationship between users and rumors, user node information, and rumor-topic information in low-dimensional space. To capture the impact of human emotional cognition during rumor spreading, we also introduce a deep-learning model based on the natural language texts of rumor topics, which analyzes the sentiment in the text and uncovers the emotional correlations among users. Furthermore, a rumor-propagation prediction model based on the text-sentiment analysis-graph convolutional network (TSA-GCN) is proposed and pre-trained on existing rumor-topic data to ensure its prediction accuracy. Finally, considering the data sparsity at a rumor-topic outbreak, the trained propagation model is transferred to the rumor topic for prediction. Meanwhile, the rumor topic in different domains has different edges and conditional distribution, similar emotional characteristics, and network structure among the rumor topics. After fine-tuning the parameter and adding a domain adaptation layer in TSA-GCN, a domain adaptation model based on parameter and graph-structure migration is obtained.

The spread of malicious information is highly destructive to society. In this article, we propose a prediction model for malicious information dissemination based on user behavior analysis. We analyze the potential connection between users and malicious information and the implicit relationship between features. First, considering the complex relationship between users and malicious information, and the fact that knowledge learning can effectively capture this dynamic, this article proposes a method to represent the implicit connection between users and information as vectors, in which the Trans-H knowledge representation learning algorithm is used to map the implicit relationships between users and malicious information onto a low-dimensional relational plane, achieving vectorized representation. Second, considering the interactions between different user features, this article uses a behavioral analysis network to solve the problem of insufficient expression ability of users' original features. This network can be used to predict users' preforwarding behaviors. Finally, time slicing is introduced for the timeliness of malicious information, and the diffusion stage of information is discretized, while the behavioral analysis network is populated according to the user's preforwarding behavior, and a kind of information-user feature matrix is constructed. Then, combined with the convolutional neural network on local spatiotemporal features, this article proposes a prediction model TS-CNN. Experiments show that on publicly available benchmark datasets, comparative experiments with existing popular methods reveal that our approach achieves significant improvements in accuracy, recall, and F1-score, thereby validating the effectiveness of our proposed relationship modeling and optimization method. The model cannot only effectively predict users' dissemination behaviors for information but also more realistically reflect the hidden factors that drive users' dissemination.

In social networks, studying methods for predicting the popularity of malicious information can help improve the ability to predict online public opinion. This paper proposes a malicious information popularity prediction model based on user influence, targeting the cooperative adversarial nature of malicious information propagation, the problem of assessing user influence in malicious information propagation space, and the complexity of malicious information propagation space. First, regarding the cooperative adversarial nature of the malicious information propagation process, considering that user behavior is influenced by both malicious and positive information during the propagation process, evolutionary game theory and multiple linear regression are introduced, and internal and external behavioral factors of the user are synthesized to construct influential functions that quantify malicious information and positive information. Meanwhile, the influence matrix is introduced when quantifying information to construct a weighted malicious information propagation network further. Second, regarding the problem of assessing user influence in the malicious information propagation space, considering the advantages of PageRank in measuring the importance of web pages and combining the timeliness of malicious information propagation, an improved algorithm T-PageRank (Timeliness-PageRank) based on timeliness is proposed. Introducing the time decay factor into the PageRank algorithm effectively enhances the accuracy and timeliness of the influence assessment of malicious information propagation. Finally, regarding the complexity of the propagation space of malicious information and considering that Graph Attention Network (GAT) can effectively capture complex relationships between nodes, combined with user influence, a malicious information popularity prediction model based on GAT is constructed. The model learns the complex interaction between users by using GAT and updates the feature representation of users so that it can be used for subsequent malicious information popularity prediction tasks. The experiment shows that the model can not only accurately assess the influence of users but also effectively predict the popularity of malicious information propagation.

Federated recommender systems have been crucially enhanced through data sharing and continuous model updates, attributed to the pervasive connectivity and distributed computing capabilities of Internet of Things (IoT) devices. Given the sensitivity of IoT data, transparent data processing in data sharing and model updates is paramount. However, existing methods fall short in tracing the flow of shared data and the evolution of model updates. Consequently, data sharing is vulnerable to exploitation by malicious entities, raising significant data privacy concerns, while excluding data sharing will result in suboptimal recommendations. To mitigate these concerns, we present LIBERATE, a privacy-traceable federated recommender system. We design a blockchain-based traceability mechanism, ensuring data privacy during data sharing and model updates. We further enhance privacy protection by incorporating local differential privacy in user–server communication. Extensive evaluations with the real-world data set corroborate LIBERATE’s capabilities in ensuring data privacy during data sharing and model update while maintaining efficiency and performance. Results underscore blockchain-based traceability mechanism as a promising solution for privacy preserving in federated recommender systems.

The open and free nature of online platforms presents challenges for tracing malicious information. To address this, we propose a traceability model based on neighborhood similarity and multitype interaction. First, we propose neighborhood similarity algorithms (D-NTC) to address the universality of malicious information dissemination. This algorithm evaluates the impact of user node importance on malicious information propagation by combining node degrees and the topological overlap of neighboring nodes. Second, we consider the interactive nature of multiple types of elements in the network and construct an interactive module based on user-path-malicious information. This module effectively captures the mutual influence relationships among diverse elements. Additionally, we employ representation learning to optimize the transition probability matrix between elements, leveraging hidden relationships to further characterize their interactive impact. Finally, we propose the NSMTI-Rank algorithm, which tackles the complexity of quantifying the influence of multiple types of elements. Drawing inspiration from mutual reinforcement effects, NSMTI-Rank comprehensively quantifies element influence through an iterative framework. Experimental results demonstrate the effectiveness of our approach in mining user node importance and capturing the interaction information among diverse elements in the network. Moreover, it enables the timely and effective identification of sources of malicious information dissemination.

This article is inspired by image forgery recognition techniques. If we regard topic comments as image pixels, the whole topic is a complete image. The image differences between rumor topics and nonrumor topics are reflected in image pixels just like forged images, and then, the problem of detecting rumor topics can be regarded as the problem of recognition images of rumor topics. First, the Topic2Image algorithm is proposed to use the semantic information to quantify the adversarial intensity among comments. It is mapped to the topological relationship among user comments. Also, the relative positions of the comment nodes are determined by the adversarial intensity. Second, considering the competitive relationship between positive and negative comments, a sentimental mutual influence model is proposed. Based on the evolutionary game theory, a transfer matrix of sentimental mutual influence is constructed. Internal and external factors of rumor detection are considered at the individual and group levels, respectively. Finally, considering the advantages of convolutional neural network (CNN) for image processing, a simple rumor detection algorithm topic image rumor detection (TIRD) based on topic image classification is proposed. Using CNNs and gray-level co-occurrence matrix to extract global and local features of topic images and combining them with the transfer matrix of sentimental mutual influence, the detection of topic rumor is realized. Experiments demonstrate the feasibility of transforming topic rumors into image. In addition, the effectiveness of image forgery recognition technology for detecting rumors is verified.

In social networks, topics often demonstrate a “fission” trend, where new topics arise from existing ones. Effectively predicting collective behavioral patterns during the dissemination of derivative topics is crucial for public opinion management. Addressing the symbiotic, antagonistic nature of “native-derived” topics, a derivative topic propagation model based on representation learning, topic relevance is proposed herein. First, considering the transition in user interest levels, cognitive accumulation at different evolutionary stages of native-derivative topics, a user content representation method, namely DTR2vec, is introduced, based on topic-related feature associations, for learning user content features. Then, evolutionary game theory is introduced by recognizing the symbiotic, antagonistic nature of “native-derived” topics during their propagation. Moreover, implicit relationships between users are explored, user influence is quantified for learning user structural features. Finally, considering the graph convolutional network’s ability to process non-euclidean structured data, the proposed model integrates user content, structural features to predict user forwarding behavior. Experimental results indicate that the proposed model not only effectively predicts the dissemination trends of derivative topics but also more authentically reflects the association, game relationships between native, derivative topics during their dissemination.

The Internet of Things (IoT) generates a substantial volume of unlabeled personal privacy data in finance and healthcare, distributed across diverse locations and networks, which is currently underutilized. Semisupervised federated learning emerges as a promising solution by conducting model training on local devices without transmitting raw data to a central server. This approach enhances the security and efficiency of IoT systems. First, to overcome traditional data augmentation limitations in simulating raw data distribution, we introduce a data augmentation module using a uniformly distributed dropout (Uout) layer. This module enhances data diversity by mitigating sensitivity to variance shifts. Furthermore, considering the insufficiency of pseudo-label availability under the condition of low-density separation, we propose the Cycle-Fed model with dual-reliability. This model enhances its performance through the effects of data augmentation by incorporating pseudo-labeled positive samples subjected to secondary validation by discriminators provided by the data augmentation module. Finally, we propose a client-side optimal value avoidance strategy based on an adaptive local proximal term, which is denoted as$\mu _{t}$. Experimental results on a public dataset indicate that the Cycle-Fed model surpasses the baseline with a 4.52%-8.76% reduction in loss, 1.62%-6.09% accuracy improvement, and a 1.285%-1.396% increase in area under the curve.

This article draws inspiration from deep learning image restoration technology. If users involved in the rumor topic are regarded as pixels in an image, the uncertainty of user behavior is similar to the ambiguity of pixels in a mosaic image. The prediction of user behavior is influenced by the user and neighboring friends. Similarly, the recovery of mosaic image pixels is also influenced by these pixels and neighboring pixels. Thus, during rumor propagation, the prediction of user behavior is equivalent to the restoration of pixels in the mosaic image. Based on this inspiration, this study proposes a rumor propagation prediction model based on image restoration technology. First, we propose the concept of topic images and design the rumor2pixel algorithm to pixelate the topic of rumor propagation. Second, through the Generative Adversarial Network model, fuzzy pixels in the “rumor topic image” are compensated to learn more realistic rumor propagation trends. Finally, a dynamic approach for predicting the propagation of rumor and countering it based on evolutionary game theory is proposed, named Rumor-DPM (rumor dynamic propagation model). This approach is focused on reconstructing rumor images while taking into account the conflict between rumors and anti-rumors as well as its timeliness. The experimental findings demonstrate that this strategy can more accurately depict the internal dynamics between rumors and anti-rumors and effectively and successfully improve the ability to forecast user behavior throughout the rumor-propagation process.

This work is inspired by high-definition (HD) image generation techniques. When the user's interests are viewed as different frames of varying clarity, the unclear parts of one interest frame can be clarified by other interest frames. The user's overall HD interest portrait can be viewed as a fusion of multiple interest frames through detail compensation. Based on this inspiration, we propose a model for generating HD interest portrait called interest frame for recommendation (IF4Rec). First, we present a fine-grained pixel-level user interest mining method, Pixel embedding (PE) uses positional coding techniques to mine atomic-level interest pixel matrices in multiple dimensions, such as time, space, and frequency. Then, using an atomic-level interest pixel matrix, we propose Item2Frame to generate several interest frames for a user. The similarity score of each item is calculated to fill the multi-interest pixel clusters, through an improved self-attention mechanism. Finally, stimulated by HD image generation techniques, we initially present an interest frame noise compensation method. By utilizing the multihead attention mechanism, pixel-level optimization and noise complementation are performed between multi-interest frames, and an HD interest portrait is achieved. Experiments show that our model mines users' interests well. On five publicly available datasets, our model outperforms the baselines.

In cloud-assisted electronic health (eHealth) systems, the exponential growth of electronic health records (EHRs) has prompted healthcare organizations to move it to the cloud. However, EHRs are encrypted before being outsourced for privacy. Although searchable encryption schemes for EHRs have been proposed, their search efficiency and functionality for massive EHRs with high-dimensional are still insufficient. In this paper, we adopt an attribute hierarchy structure for medical datasets, enabling efficient multi-dimensional range search and reducing high-dimensional EHRs to low-dimensional vectors. To further improve search efficiency, we design an index tree that require no additional storage and computational overhead, significantly improving efficiency in search, trapdoor generation, and index building. Our scheme is well-suited for large-scale medical data scenarios, especially in dealing with high-dimensional and massive datasets. Extensive experiments demonstrate the superiority of our scheme over existing solutions, particularly in large-scale medical data scenarios. Compared to the classic EDMRS scheme, our scheme has a computational overhead in index building and search that is only about 1/500 and 1/10 of EDMRS when the number of keywords and electronic health records is 3,000 and 6,000, respectively. Moreover, as medical data and keywords increase, our scheme shows slower computational overhead growth compared to EDMRS.

Recommendation systems greatly improve user experience with personalized suggestions. Much research utilizes deep learning to extract user interest features, yet often neglects their real-time and dynamic aspects. This paper proposes a joint learning-based recommendation model (JLS-Rec) for e-commerce platforms, integrating both short-term and long-term user interests. First, for users' long-term interests, the paper suggests mining more detailed interests from user behavior sequences. This method decouples the behavior sequence in horizontal and vertical directions using a Convolutional Neural Network to learn the user's long-term interests. Second, for users' short-term interests, the JLS-Rec method learns feature transformations on users' recent behavior sequences by stacking multiple self-attention mechanisms, resulting in dynamic representations of the user's short-term interests at the current stage. Finally, based on the principle of prioritizing short-term memory without neglecting long-term interests, the paper proposes a joint learning framework with dual embeddings to balance the two characteristics of user long-term and short-term interests. This framework generates accurate recommendation results while utilizing these two interest features to predict user feedback on products. The experimental results demonstrate that the model effectively mines the long-term and short-term interest information of users in the features, thereby improving the recommendation accuracy of e-commerce platforms.

The spread of online rumor poses challenges to social peace and public order. Traditional research on rumor diffusion commences from the rumor itself, without considering the symbiosis and confrontation of anti-rumor and motivation-rumor. This study proposed a diffusion method for online rumor based on three messages: rumor, anti-rumor, and motivation-rumor. First, considering the ability of representation learning to learn unsupervised features, we decided to use representation learning method to the diversity and complexity of the content and structure feature space. In particular, we designed a new representation method—Rumor2vec—for the potential structural feature of the rumor diffusion network. Second, considering the mutual promotion and suppression of the three messages, we constructed a new network topology using the cooperative and competitive relationships based on the evolutionary game theory. Finally, considering the ability of graph convolutional network (GCN) to convolute non-Euclidean structure data such as social network, and in view of the time effectiveness of topic evolution, this study proposed a dynamic and game-GCN (evolutionary game theory GCN)-based rumor diffusion model. Experiments show that the model can not only predict the group behavior under the rumor topic but also accurately reflect the cooperation and competition among multiple messages.

In online social networks, users’ awareness of a topic and their personal consciousness can significantly influence the dissemination of information on that topic. Traditional information dissemination research has mainly introduced user awareness as a single factor and has failed to fully consider the impact between users with different awareness. By considering the differences in user awareness and their impact, we proposed a multiple layer spatial information dissemination model based on user awareness. First, for the multidimensional nature of the communication space, we consider differences in users’ cognitive level into consideration, and by setting the corresponding threshold, the users who participate in topic dissemination are divided into two states of high awareness and low awareness. By constructing different communication networks, we depict the propagation dynamics of information at different levels of awareness and introduce an evolutionary game mechanism to analyze the influence between users at different levels of awareness networks. Second, during information dissemination, isolating transmission sources can provide effective control over the spread of information. Based on the susceptible-infectious-recovery (SIR) model in infectious disease dynamics, we consider that susceptible users have a certain chance of being isolated, so we abstract the isolated user group as an independent state Q (quarantine) and introduce it into the traditional SIR model to construct an SQIR information dissemination model. Finally, considering the influence between the information disseminated by high awareness users and low awareness users and the differences in users’ communication behavior at different periods, a dynamic information dissemination model based on user awareness and evolutionary game is obtained through a time-slicing process to dynamically explore the dynamics of information dissemination. The experiments showed that the proposed model could effectively reveal the influence of different levels of awareness on the dynamic information dissemination trend.

In online social networks, rumors have become a flashpoint for public opinion. This article proposes a rumor and anti-rumor diffusion model based on multi-information multidimensional compound game considering the complex game antagonism in the process of spreading multiple messages. First, in view of the diversity and complexity of the characteristic space of communication individual, communication structure, and multitype rumor and anti-rumor news, the communication network is expressed in low-dimensional, real-value, and dense vectorization by combining the representation learning algorithm and centering on the content, structure, and attribute of information transmission. Second, considering the antagonistic and competitive relationship between multiple rumors and anti-rumors, and considering the compound iterative cascade of multi-information rumor, this article proposes a multi-information rumor and anti-rumor game model from a multidimensional perspective. Finally, the information expression of the topic space and the complex game relationship between multiple messages are considered comprehensively. At the same time, considering the graph convolutional network (GCN)’s ability of convolution processing non-Euclidean data such as social network, a dynamic, unified representation, multimessage complex game topic propagation model MMGameGCN is proposed based on graph convolutional neural network (MultiMessage-game GCN). The experiment shows that the model can more truly and effectively reflect the process of network rumor propagation in the multimessage transmission network, effectively analyze the user group behavior of rumor topic under multiple messages, and correctly perceive the spread situation of rumor and anti-rumor.

This study is inspired by the current image restoration technology. If we regard the users participating in the rumor as image pixels, similar to social networks, the recovery of pixel data is affected by the pixels themselves and neighbor pixels, then the prediction of user behavior in the rumor diffusion can be regarded as the process of image restoration for pixel-blurred user behavior images. We first propose a diffusion2pixel algorithm that transforms the user relationship network of topic diffusion into image pixel matrix. To cope with the diversity and complexity of the diffusion feature space, the user relationship network is reduced to a low-rank dense vectorization by representation learning before being pixelated by cutting and diffusion. Second, considering the competitive relationship between rumor and anti-rumor, transition matrix of rumor mutual influences is established by evolutionary game theory. A mutual influence model of rumor and anti-rumor is then proposed. Finally, we combine the transition matrix of rumor mutual influence into a simple prediction method Graph-CNN of rumor and anti-rumor topic diffusion based on dynamic iteration mechanism. Experiments confirmed the proposed model can effectively predict the group diffusion trends of rumor, and reflects the competitive relationship between rumor and anti-rumor.

With the increasing network service content, innovative methods are required for developing optimized network service for e-commerce companies. Accordingly, this study focuses on designing a framework containing personalization, interest group identification, and recommendation mechanisms. The primary contribution of this paper is to propose a recommendation model based on data compensation and dynamic user interest grouping. First, to address the problem of sparse user rating data, homeostasis compensation is performed on native data to more realistically restore the preference relationship between users and items by introducing the advantages of generative adversarial network in learning data distribution and enhancing data samples. Second, to address the problem of user interest generalization, information entropy is introduced to measure the user interest feature space. In addition, the time window marking method is used to further quantify the users’ dynamic interest group around the users’ interest drift. Finally, considering tensor decomposition characteristics in data dimension transformation and data compression, a score prediction model based on the “user-item-interest group” tensor decomposition is constructed. Simultaneously, a time decay function is introduced in the construction of the tensor to dynamically fit the user behavior and further improve prediction accuracy. Experiments show that the proposed framework can effectively improve the recommendation accuracies resulting from both sparse scoring data and dynamic user interest division.

In the field of social information dissemination, current studies are mainly based on the network topology with an explicit user link. Implicit link in this study indicates users who often participate in the same topic due to certain interests or game relationships, but no explicit “following-followed” link exists among these users. In this article, the implicit link is regarded as one of the driving factors of users to participate in information dissemination, and then the network topology with the implicit link is established. First, we excavate implicit relationships among users to strengthen friends' influence in driving information dissemination and then establish a more accurate network topology. Second, we extract the individual and friend driving mechanisms based on the accurate network topology, analyze the causes of two information dynamics, and measure social influence based on a multiple linear regression model. Finally, considering the timeliness and uncertainty of information dissemination in an infectious disease model, we introduce the mean-field theory and obtain an information dissemination model based on social influence. The experimental results show that the implicit link plays an important role in driving user behavior, and the model can well explain the process of information transmission and explore the dynamic factors of information dissemination.

In network public-opinion analysis, the diversity of messages under social hot topics plays an important role in user participation behavior. Considering the interactions among multiple messages and the complex user behaviors, this article proposes a prediction model of user participation behavior during multiple messaging of hot social topics. First, considering the influence of multimessage interaction on user participation behavior, a multimessage interaction influence-driving mechanism was proposed to predict user participation behavior more accurately. Second, in the view of the behavioral complexity of users engaging in multimessage hotspots and the simple structure of backpropagation (BP) neural networks (which can map complex nonlinear relationships), this study proposes a user participant behavior prediction model of social hotspots based on a multimessage interaction-driving mechanism and the BP neural network. Finally, the multimessage interaction has an iterative guiding effect on user behavior, which easily causes overfitting of the BP neural network. To avoid this problem, the traditional BP neural network is optimized by a simulated annealing algorithm to further improve the prediction accuracy. In evaluation experiments, the model not only predicted the user participation behavior in actual situations of multimessage interaction but also further quantified the correlations among multiple messages on hot topics.

The traditional rumor diffusion model primarily studies the rumor itself and user behavior as the entry points. The complexity of user behavior, multidimensionality of the communication space, imbalance of the data samples, and symbiosis and competition between rumor and anti-rumor are challenges associated with the in-depth study on rumor communication. Given these challenges, this study proposes a group behavior model for rumor and anti-rumor. First, this study considers the diversity and complexity of the rumor propagation feature space and the advantages of representation learning in the feature extraction of data. Further, we adopt the corresponding representation learning methods for their content and structure of the rumor and anti-rumor to reduce the spatial feature dimension of the rumor-spreading data and to uniformly and densely express the full-featured information feature representation. Second, this paper introduces an evolutionary game theory, which is combined with the user-influenced rumor and anti-rumor, to reflect the conflict and symbiotic relationship between rumor and anti-rumor. we obtain a network structural feature expression of the influence degree of users on rumor and anti-rumor when expressing the structural characteristics of group communication relationships. Finally, aiming at the timeliness of rumor topic evolution, the whole model is proposed. Time slice and discretize the life cycle of rumor is used to synthesize the full-featured information feature representation of rumor and anti-rumor. The experiments denote that the model can not only effectively analyze user group behavior regarding rumor but also accurately reflect the competition and symbiotic relation between rumor and anti-rumor diffusion.

Forwarding is the keyway for information propagation in social networks that affected by complex factors. Due to the fact that the forwarding mechanisms are essentially unclear, this paper focuses on the formation and evolution of the external as well as internal driving mechanisms and develops a Gaussian dynamic topic model for forwarding prediction by incorporating all the information of nodes and edges. First, based on the diversity of communities each user located in, latent Dirichlet allocation (LDA) traditional text modeling method is applied to user following relationships modeling that leads to the initial formation of communities, and user interacting relationships modeling that leads to the evolution of communities. Taking the advantage of LDA topic model in dealing with the problem of polysemy and synonym, we can mine user latent distribution over communities and analyze the external community driving effects. Second, considering the differences in individual habits, time factor is introduced and the dynamic topic model is proposed to model user behavioral attributes. Meanwhile, in regard to continuous user attributes modeling, the parameter of topic-word distribution in the topic model is replaced by multivariate Gaussian distributions, which shown to be effective at capturing the regularities of individual behavioral habits and analyzing the internal individual driving effects. Finally, combining with external and internal factors, a probabilistic graph model is used to modeling forwarding behavior. We propose methods based on Gibbs sampling and an expectation–maximization algorithm to estimate model parameters and fit our model to predict user forwarding actions. Experimental results indicate that the model can not only detect the latent communities but also can improve the performance of forwarding prediction effectively.

Exploring the dynamics of the diffusion of multiple social networking messages associated with the same topic has become the focus of current research. Given the interaction among multiple messages involved in a diffusion process and the rich intertwined effects, a diffusion model based on multiple messages and multiplex network space is proposed. The proposed model attempts to explore the internal mechanism related to concurrent diffusion of multiple messages relative to social topics. First, aiming at the multidimensional characteristics of the diffusion space, the overlap of social structure, and the complexity of diffusion behavior are mined and mapped to a multiplex diffusion space. Through hierarchical processing, multidimensional message diffusion paths can be constructed, and the diffusion situation of each message can be further perceived. In addition, because, in information diffusion, the infectious rate depends on the time change, the time-dependent variables associated with interaction among multiple messages are introduced. In this manner, the diffusion process of mutual promotion (cooperative relationship) and restraint (competitive relationship) between multiple messages can be revealed. Finally, considering information interaction and diffusion network complexity, a dynamic interaction mechanism based on the multiplex SIS diffusion model is introduced. A unified framework of multiple messages coupled dynamic processes is constructed using a discrete-time microscopic Markov chain approach (MMCA). Experimental results indicate that the framework can reveal the mechanism of multiple message interaction in social topics more realistically.

In social networks, the identification of key elements in a topic propagation network, including key messages, paths, and users, is important in network public opinion mining and control. In view of the three key elements, this paper proposes a social hotspot tracking scheme termed MPURank, which is based on tripartite graphing and a multimessage-driven iterative method. First, the topic is regarded as a network of simultaneous multimessage dissemination, and retweeting topology graphing is used to determine the propagation path; then, a topic tripartite graph is established to elucidate the interrelationship among the nodes of messages, paths, and users. Second, an iterative scoring algorithm based on hyperlink-induced topic search is proposed to rank the key elements based on the tripartite graph. Using the initial score vectors of different elements and weight matrices, the algorithm obtains the final score sequences of the elements. This algorithm circumvents the influence of the multimessage network and propagation path on the evolution of public opinion, overcoming the issues of node attributes and topics, and the influence of multimessage and multipath complexity in source tracing, thus identifying the key elements. Finally, we conducted an experiment based on real-world data, which verifies the utility of the algorithm.

Road networks are extremely vulnerable to cascading failure caused by traffic accidents or anomalous events. Therefore, accurate identification of critical nodes, whose failure may cause a dramatic reduction in the road network transmission efficiency, is of great significance to traffic management and control schemes. However, none of the existing approaches can locate city-wide critical nodes in real road networks. In this paper, we propose a novel data-driven framework to rank node importance through mining from comprehensive vehicle trajectory data, instead of analysis solely on the topology of the road network. In this framework, we introduce a trip network modeled by a tripartite graph to characterize the dynamics of the road network. Furthermore, we present two algorithms, integrating the origin-destination entropy with flow (ODEF) algorithm and the crossroad-rank (CRRank) algorithm, to better exploit the information included in the tripartite graph and to effectively assess the node importance. ODEF absorbs the idea of the information entropy to evaluate the centrality of a node and to calculate its importance rating by integrating its centrality with the traffic flow. CRRank is a ranking algorithm based on eigenvector centrality that captures the mutual reinforcing relationships among the OD-pair, path, and intersection. In addition to the factors considered in ODEF, CRRank considers the irreplaceability of a node and the spatial relationships between neighboring nodes. We conduct a synthetic experiment and a real case study based on a real-world dataset of taxi trajectories. Experiments verify the utility of the proposed algorithms.

Abstract In view of the complex relationships involved in network structures, as well as the Markovian nature of information diffusion and the complexity of diffusion paths in social hotspots, we introduce and optimize a basic structure in social networks—the triad. A group behavior diffusion model is proposed based on the optimal triadic structure and factor graph. In addition to predicting individual participation behavior, the model forecasts group development trends of hotspots. First, considering the dynamics of social topic information diffusion, an information triad structure is proposed based on the traditional structure for analyzing the complex influencing factors. Second, in accordance with the Markovian nature of information diffusion, a group behavior diffusion model is proposed associated with the basic concepts and methods of random field theory. This model can not only capture the strengths of different influencing factors but also mine the dynamic patterns of topics. The experimental results reveal the efficiency of our model in both predicting individual participation behavior and perceiving group evolution trends in social hotspots.

The exponential growth of devices connected to the network has resulted in the development of new Internet of Things (IoT) applications and online services, which may have diverse and dynamic requirements on received quality. Although, the emerging software-defined networking (SDN) approach can be leveraged for the IoT environment, to dynamically achieve differentiated quality levels for different IoT tasks in very heterogeneous wireless networking scenarios, the open interfaces in SDN introduces new network attacks, which may make SDN-based IoT malfunctioned. The challenges lies in securely using SDN for IoT systems. To address this challenge, we design a SDN-based data transfer security model middlebox-guard (M-G). M-G aims at reducing network latency, and properly manage dataflow to ensure the network run safely. First, according to different security policies, middleboxes related to the defined secure policies, are placed at the most appropriate locations, using dataflow abstraction and a heuristic algorithm. Next, to avoid any middlebox becoming a hotspot, an offline integer linear program (ILP) pruning algorithm is proposed in M-G, to tackle switch volume constraints. In addition, an online linear program (LP) formulation is come up to handle load balance. Finally, secure mechanisms are proposed to handle different attacks. And network routing is solved flexibly, through dataflow management protocol, which are formulated via combining tunnels and tags. Experimental results demonstrate that this model can improve security performance and manage dataflow effectively in SDN-based IoT system.

In social networks, link establishment among the users is affected by complex factors. In this paper, we try to investigate the internal and external factors that affect the formation of links and propose a three-level hidden Bayesian link prediction model by integrating the user behavior as well as user relationships to link prediction. First, based on the user multiple interest characteristics, a latent Dirichlet allocation (LDA) traditional text modeling method is applied into user behavior modeling. Taking the advantage of LDA topic model in dealing with the problem of polysemy and synonym, we can mine user latent interest distribution and analyze the effects of internal driving factors. Second, owing to the power-law characteristics of user behavior, LDA is improved by Gaussian weighting. In this way, the negative impact of the interest distribution to the high-frequency users can be reduced and the expression ability of interests can be enhanced. Furthermore, taking the impact of common neighbor dependencies in link establishment, the model can be extended with hidden naive Bayesian algorithm. By quantifying the dependencies between common neighbors, we can analyze the effects of external driving factors and combine internal driving factors to link prediction. Experimental results indicate that the model can not only mine user latent interest distribution but also can improve the performance of link prediction effectively.

Genome-wide Association Study has presented a promising way to understand the association between human genomes and complex traits. Many simple polymorphic loci have been shown to explain a significant fraction of phenotypic variability. However, challenges remain in the non-triviality of explaining complex traits associated with multifactorial genetic loci, especially considering the confounding factors caused by population structure, family structure, and cryptic relatedness. In this paper, we propose a Squared-LMM (LMM2) model, aiming to jointly correct population and genetic confounding factors. We offer two strategies of utilizing LMM2for association mapping: 1) It serves as an extension of univariate LMM, which could effectively correct population structure, but consider each SNP in isolation. 2) It is integrated with the multivariate regression model to discover association relationship between complex traits and multifactorial genetic loci. We refer to this second model as sparse Squared-LMM (sLMM2). Further, we extend LMM2/sLMM2by raising the power of our squared model to the LMMn/sLMMnmodel. We demonstrate the practical use of our model with synthetic phenotypic variants generated from genetic loci of Arabidopsis Thaliana. The experiment shows that our method achieves a more accurate and significant prediction on the association relationship between traits and loci. We also evaluate our models on collected phenotypes and genotypes with the number of candidate genes that the models could discover. The results suggest the potential and promising usage of our method in genome-wide association studies.