Partial label learning efficiently extracts accurate labels from weak supervision, where each instance has multiple candidate labels but only one is correct. Existing partial label learning methods typically employ a two-stage process and fall into a suboptimal solution in high-dimensional settings, lacking an effective feedback mechanism between feature selection and label disambiguation. Nevertheless, existing methods suffer from at least one of two issues, i.e., redundant features and the uncertainty of label disambiguation. To address these problems, we propose a unified and deep partial label feature selection method with CLuster-guided disAmbiguation and Structured Sparsity (CLASS), which simultaneously preserve discriminative features and promotes candidate label disambiguation by bidirectional optimization. Specifically, we integrate nonlinear deep networks to capture high-order semantic relations and feature interactions, while employing a linear sparse gating mechanism to preserve the label-specific, discriminative and interpretable features. Moreover, a cluster-guided disambiguation module is designed to enforce inter-class global separation and intra-class local cohesion via distance constraint and confidence penalty and dynamically align predicted labels with the semantics of class prototypes. Extensive experimental results validate the superiority and effectiveness of our proposed CLASS over state-of-the-art methods.

Source localization has attracted increasing attention in recent years due to its vital role in governing the harmful propagation. However, existing localization methods do not fully consider the temporal characteristics in propagation and struggle to leverage the continuous-time information of real-world propagation scenarios. Moreover, the aggregation ability of GNN based localization models is limited by the structural noise commonly present in complicated real-world topologies. To address these challenges, a Spectral Neural Ordinary Differential Equation (SNODE) is proposed to infer the source in real-world continuous-time scenarios. First, the forward propagation is formulated as a flow based ODE system, and the source localization problem is transformed into an inverse ODE modeling task. Second, a neural process based on a graph variational autoencoder is introduced to encode global latent propagation patterns as a conditioning variable for the ODE system. Third, a spectral graph optimization is performed to suppress topological noise by filtering out high-frequency components that degrade the quality of graph aggregation in the neural process. Comprehensive experiments demonstrate that SNODE not only outperforms the optimal baseline in real-world continuous-time propagation scenarios with an average performance improvement of 43.8%, but also achieves consistently superior performance in synthetic discrete-time datasets with an improvement of 4.5%, highlighting its strong generalization ability in different propagation settings. Our code is available at https://github.com/cgao-comp/SNODE.

Defending against backdoor attacks on graphs has become increasingly critical. Existing methods predominantly focus on detecting and removing triggers by identifying inconsistencies between trigger and clean nodes. However, adversaries can design triggers that closely resemble clean nodes, making them challenging to detect. Therefore, understanding the mechanisms underlying backdoor attacks is crucial. In this work, we observe an interesting phenomenon: in backdoored models, specific ''backdoor neurons'' (embedding dimensions) are more likely to be activated, causing nodes to be misclassified to the target label. This is largely due to the graph structure, where malicious information propagates through node neighborhoods, activating specific neurons and target label. Based on this observation, we theoretically and empirically demonstrate how graph backdoor attacks exploit this propagation mechanism to effectively poison the target node's embedding. Meanwhile, we propose a novel defense called Graph Backdoor Neuron Defense (GBND) to identify, unlearn, and recover backdoor neurons. Specifically, we design a novel reverse engineering technique to identify triggers that activate backdoor neurons, and eliminate their harmful effects by asymmetric unlearning and recovering at the neuron level. Extensive experiments on four datasets validate the effectiveness of GBND in defending against backdoor attacks.

The rapid spread of fake news on social media has significantly increased the importance of computational detection methods. Graph-based approaches, particularly Graph Neural Networks (GNNs), have emerged as powerful tools for modeling news propagation patterns. Despite their potential, current GNN-based methods still face challenges in robustness and interpretability due to two key shortcomings: they inadequately filter out irrelevant user-induced noise within propagation graphs, and their shallow architectures fail to effectively capture the intricate long-range dependencies characteristic of news propagation. To overcome these limitations, we propose NEGT (Noise-filtering Enhanced Graph Transformer), a novel graph Transformer framework explicitly designed for fake news detection. NEGT introduces a noise-augmented information bottleneck strategy embedded within its self-attention mechanism, effectively identifying and removing task-irrelevant interactions. Additionally, we propose a novel relational propagation graph encoding a strategy that explicitly captures multi-scale user relationships and propagation depth, enabling NEGT to model long-sequence propagation dependencies accurately. Experiments on various benchmark datasets show that NEGT surpasses current methods in accuracy, noise robustness, and interpretability.

With the rapid global growth of short video platforms, the spread of fake news has become increasingly prevalent, creating an urgent demand for effective automated detection methods. Current approaches typically rely on feature extractors to gather information from multiple modalities and then generate predictions through classifiers. However, these methods often fail to fully utilize the complex information across all modalities and overlook the potential for video manipulation, limiting their overall performance. To tackle these issues, MFAE is proposed, a novel framework for Multimodal Feature Adaptive Enhancement for Fake News Video Detection. The framework starts by extracting semantic and emotional features from the news, which are the basis for generating coarse multimodal representations. These representations are further refined through Adaptive Enhancement, a module specifically designed to strengthen the visual and audio modalities. Subsequently, spatial and temporal features are extracted separately, with temporal features undergoing additional refinement via a Temporal Enhancement module. The final result is obtained by feeding the individually enhanced features into the multimodal feature integration module for interaction Comprehensive experiments on two benchmark datasets highlight the exceptional performance of MFAE in detecting fake news on short video platforms. Specifically, the method achieves accuracy improvements of 2.21% and 4.35% on FakeSV and FakeTT, respectively.

With the expansion of business scales and scopes on online platforms, multi-scenario matching has become a mainstream solution to reduce maintenance costs and alleviate data sparsity. The key to effective multi-scenario recommendation lies in capturing both user preferences shared across all scenarios and scenario-aware preferences specific to each scenario. However, existing methods often overlook user-specific modeling, limiting the generation of personalized user representations. To address this, we propose PERSCEN, an innovative approach that incorporates user-specific modeling into multi-scenario matching. PERSCEN constructs a user-specific feature graph based on user characteristics and employs a lightweight graph neural network to capture higher-order interaction patterns, enabling personalized extraction of preferences shared across scenarios. Additionally, we leverage vector quantization techniques to distill scenario-aware preferences from users' behavior sequence within individual scenarios, facilitating user-specific and scenario-aware preference modeling. To enhance efficient and flexible information transfer, we introduce a progressive scenario-aware gated linear unit that allows fine-grained, low-latency fusion. Extensive experiments demonstrate that PERSCEN outperforms existing methods. Further efficiency analysis confirms that PERSCEN effectively balances performance with computational cost, ensuring its practicality for real-world industrial systems.

Popularity bias occurs when popular items are recommended far more frequently than they should be, negatively impacting both user experience and recommendation accuracy. Existing debiasing methods mitigate popularity bias often uniformly across all users and only partially consider the time evolution of users or items. However, users have different levels of preference for item popularity, and this preference is evolving over time. To address these issues, we propose a novel method called CausalEPP (Causal Intervention on Evolving Personal Popularity) for taming recommendation bias, which accounts for the evolving personal popularity of users. Specifically, we first introduce a metric called Evolving Personal Popularity to quantify each user's preference for popular items. Then, we design a causal graph that integrates evolving personal popularity into the conformity effect, and apply deconfounded training to mitigate the popularity bias of the causal graph. During inference, we consider the evolution consistency between users and items to achieve a better recommendation. Empirical studies demonstrate that CausalEPP outperforms baseline methods in reducing popularity bias while improving recommendation accuracy.

Early rumor detection is crucial for mitigating the widespread dissemination of misinformation. Existing methods predominantly rely on complete rumor diffusion graphs, which are challenging to obtain in real-world scenarios, complicating early detection efforts. To address this challenge, we propose D2, a two-stage framework for early rumor Detection, integrating cascade Diffusion prediction. This framework aims to enhance early rumor detection by incorporating diffusion prediction capabilities. Specifically, a dynamic heterogeneous graph neural network (GNN) is developed to jointly model users' social and propagation graphs, enabling accurate prediction of potential diffusion paths using limited observed data within short time windows. The inferred diffusion paths are then integrated with early-stage data, and GNNs are employed for graph classification. However, the varying data distributions across different social media platforms necessitate extensive tuning to optimize GNN architectures. To facilitate the detection of rumor diffusion graphs at the initial stages, a search space is designed across four dimensions- aggregation, merge, readout, and sequence functions-encompassing various GNN architectures. Subsequently, D2 employs an efficient differentiable search algorithm to identify high-performance GNNs within this search space. Experimental results on real social media datasets demonstrate that this approach significantly improves both the accuracy and robustness of early rumor detection.

Graph Contrastive Learning (GCL) is a widely adopted approach in self-supervised graph representation learning, applying contrastive objectives to produce effective representations. However, current GCL methods primarily focus on capturing implicit semantic relationships, often overlooking the structural commonsense embedded within the graph's structure and attributes, which contains underlying knowledge crucial for effective representation learning. Due to the lack of explicit information and clear guidance in general graph, identifying and integrating such structural commonsense in GCL poses a significant challenge. To address this gap, we propose a novel framework called Structural Commonsense Unveiling in Graph Contrastive Learning (Str-GCL). Str-GCL leverages first-order logic rules to represent structural commonsense and explicitly integrates them into the GCL framework. It introduces topological and attribute-based rules without altering the original graph and employs a representation alignment mechanism to guide the encoder in effectively capturing this commonsense. To the best of our knowledge, this is the first attempt to directly incorporate structural commonsense into GCL. Extensive experiments demonstrate that Str-GCL outperforms existing GCL methods, providing a new perspective on leveraging structural commonsense in graph representation learning.

Hyperedges, as extensions of pairwise edges, can characterize higher-order relations among multiple individuals. Due to the necessity of hypergraph detection in practical systems, hyperedge prediction has become a frontier problem in complex networks. However, previous hyperedge prediction models encounter three challenges: (i) failing to predict dynamic and arbitrary-order hyperedges simultaneously, (ii) confusing higher-order and lower-order features together to propagate neighborhood information, and (iii) lacking the capability to learn physical evolution laws, which lead to poor performance of the models. To tackle these challenges, we propose D$^{3}$HP, aDual-viewDesynchronization hypergraph learning for arbitrary-orderDynamicHyperedgePrediction. Specifically, D$^{3}$HP extracts the dynamic higher-order and lower-order features of hyperedges separately through an elastic hypergraph neural network (EHGNN) and an alternate desynchronization graph convolutional network (ADGCN) at each time snapshot. EHGNN is designed to incrementally mine the implicit higher-order relations and propagate neighborhood information. Moreover, ADGCN aims to combine GCN with desynchronization learining to learn the physical evolution of lower-order relations and alleviate the over-smoothing problem. Further, we improve the prediction performance of the model by rationally fusing the features learned from the dual views. Extensive experiments on 8 dynamic higher-order networks demonstrate that D$^{3}$HP outperforms 14 state-of-the-art baselines.

The incorporation of target distribution significantly enhances the success of deep clustering. However, most of the related deep clustering methods suffer from two drawbacks: (1) manually-designed target distribution functions with uncertain performance and (2) cluster misassignment accumulation. To address these issues, aSelf-CorrectingClustering (Self-CC) framework is proposed. In Self-CC, a robust target distribution solver (RTDS) is designed to automatically predict the target distribution and alleviate the adverse influence of misassignments. Specifically, RTDS divides the high confidence samples selected according to the cluster assignments predicted by a clustering module into labeled samples with correct pseudo labels and unlabeled samples of possible misassignments by modeling its training loss distribution. With the divided data, RTDS can be trained in a semi-supervised way. The critical hyperparameter which controls the semi-supervised training process can be set adaptively by estimating the distribution property of misassignments in the pseudo-label space with the support of a theoretical analysis. The target distribution can be predicted by the well-trained RTDS automatically, optimizing the clustering module and correcting misassignments in the cluster assignments. The clustering module and RTDS mutually promote each other forming a positive feedback loop. Extensive experiments on four benchmark datasets demonstrate the effectiveness of the proposed Self-CC.

Fact verification task has emerged as an essential research topic recently due to abundant fake news spreading on the Internet. The task based on unstructured data (i.e., news) has achieved great development, but the task based on structured data (i.e., table) is still in the primary development period. The existing methods usually construct complete heterogeneous graph networks around statement, table, and program subgraphs, and then infer to learn similar semantics on them for fact verification. However, they generally connect the nodes with the same content between subgraphs directly to frame a larger graph network, which has serious sparsity in connections, especially when subgraphs possess limited semantics. To this end, we propose tight-fitting graph inference network (TFGIN), which innovatively builds tight-fitting graphs (TF-graphs) to strengthen the connections of subgraphs and designs inference modeling layer (IML) to learn coherence evidence for fact verification. Specifically, different from traditional connection ways, the constructed TF-graph enhances inter-graph and intra-graph connections of subgraphs through subgraph segmentation and interaction guidance mechanisms. IML could reason the semantics with strong correlation and high consistency as explainable evidence. Experiments on three competitive datasets confirm the superiority and scalability of our TFGIN.

Source localization in social platforms is critical for managing and controlling the misinformation spreading. Despite all the recent advancements, existing methods do not consider the dynamic and heterogeneous propagation behaviors of users and are developed based on simulated data with strong model assumptions, limiting the application in real-world scenarios. This research addresses this limitation by presenting a novel framework for source localization, grounded in real-world propagation cascades from platforms like Weibo and Twitter. What's more, recognizing the user-driven nature of users in information spread, we systematically crawl and integrate user-specific profiles, offering a realistic understanding of user-driven propagation dynamics. In summary, by developing datasets derived from real-world propagation cascades, we set a precedent in enhancing the authenticity and practice of source identification for social media. Our comprehensive experiments not only validate the feasibility and rationale of our novel user-centric localization approaches but also emphasize the significance of considering user profiles in real-world propagation scenarios. The code is available at https://github.com/cgao-comp/NFSL.

The topology of diffusion networks plays an essential role in understanding information propagation dynamics and conducting social network analysis. However, diffusion networks are often unobservable in practical applications, leading to wide research on network inference from information cascades over the past decade. At present, novel cascades-based methods have been further developed to recover temporal dynamics and network topology by exploring the utilization of node temporal information, resulting in notable advancements. However, it requires high costs to acquire extensive temporal information, and the performance of network inference may decrease due to potential observational errors. Therefore, this paper specifically focuses on the time-independent scenario to address these limitations. Firstly, this paper models the node statuses of each diffusion process by leveraging the assumption of propagation trees based on the well-known independent cascade model. Subsequently, a gradient-based approach is developed to estimate the influences between nodes, facilitating the inference of network structure. Furthermore, this paper proposes a Monte Carlo EM-based approach to enhance the efficiency of network inference while maintaining comparable accuracy. Extensive experiments are conducted to verify the efficiency and effectiveness of our approaches on both synthetic and real-world networks.

Being widely used in learning unbiased visual question answering (VQA) models, Data Augmentation (DA) helps mitigate language biases by generating extra training samples beyond the original samples. While today's DA methods can generate robust samples, the augmented training set, significantly larger than the original dataset, often exhibits redundancy in terms of difficulty or content repetition, leading to inefficient model training and even compromising the model performance. To this end, we design an Effective Curriculum Learning strategy ECL to enhance DA-based VQA methods. Intuitively, ECL trains VQA models on relatively "easy'' samples first, and then gradually changes to "harder'' samples, and less-valuable samples are dynamically removed. Compared to training on the entire augmented dataset, ECL strategy can further enhance VQA models' performance with fewer training samples. Extensive ablations have demonstrated the effectiveness of ECL on various methods.

To make Graph Neural Networks (GNNs) meet the requirements of the Web, the universality and the generalization become two important research directions. On one hand, many universal GNNs are presented for semi-supervised tasks on both homophilic and non-homophilic graphs by distinguishing homophilic and heterophilic edges with the help of labels. On the other hand, self-supervised learning (SSL) algorithms on graphs are presented by leveraging the self-supervised learning schemes from computer vision and natural language processing. Unfortunately, graph universal self-supervised learning remains resolved. Most existing SSL methods on graphs, which often employ two-layer GCN as the encoder and train the mapping functions, can't alter the low-passing filtering characteristic of GCN. Therefore, to be universal, SSL must becustomized for the graph, i.e., learning the graph. However, learning the graph via universal GNNs is disabled in SSL, since their distinguishability on homophilic and heterophilic edges disappears without the labels. To overcome this difficulty, this paper proposes novel GrAph-customized Universal Self-Supervised Learning (GAUSS) by exploiting local attribute distribution. The main idea is to replace the global parameters with locally learnable propagation. To make the propagation matrix demonstrate the affinity between the nodes, the self-representative learning framework is employed with k-block diagonal regularization. Extensive experiments on synthetic and real-world datasets demonstrate its effectiveness, universality and robustness to noises.

Real-world graphs exhibit diverse structures, including homophilic and heterophilic patterns, necessitating the development of a universal Graph Contrastive Learning (GCL) framework. Nonetheless, the existing GCLs, especially those with a local focus, lack universality due to the mismatch between the input graph structure and the homophily assumption for two primary components of GCLs. Firstly, the encoder, commonly Graph Convolution Network (GCN), operates as a low-pass filter, which assumes the input graph to be homophilic. This makes it challenging to aggregate features from neighbor nodes of the same class on heterophilic graphs. Secondly, the local positive sampling regards neighbor nodes as positive samples, which is inspired by the homophily assumption. This results in feature similarity amplification for the samples from the different classes (i.e., FALSE positive samples). Therefore, it is crucial to feed the encoder and positive sampling of GCLs with homophilic graph structures. This paper presents a novel GCL framework, named gRaph cOntraStive Exploring uNiversality (ROSEN), designed to achieve this objective. Specifically, ROSEN equips a local graph structure inference module, utilizing the Block Diagonal Property (BDP) of the affinity matrix extracted from node ego networks. This module can generate the homophilic graph structure by selectively removing disassortative edges. Extensive evaluations validate the effectiveness and universality of ROSEN across node classification and node clustering tasks.

We present a new encoder-decoder generative network dubbed EdgeNet, which introduces a novel encoder-decoder framework for data-driven auction design in online e-commerce advertising. We break the neural auction paradigm of Generalized-Second-Price(GSP), and improve the utilization efficiency of data while ensuring the economic characteristics of the auction mechanism. Specifically, EdgeNet introduces a transformer-based encoder to better capture the mutual influence among different candidate advertisements. In contrast to GSP based neural auction model, we design an autoregressive decoder to better utilize the rich context information in online advertising auctions. EdgeNet is conceptually simple and easy to extend to the existing end-to-end neural auction framework. We validate the efficiency of EdgeNet on a wide range of e-commercial advertising auction, demonstrating its potential in improving user experience and platform revenue.

Graph neural network (GNN) can be formulated as the multiplication of the topology-related matrix (adjacency or Laplacian matrix) and node attribute matrix, i.e., operation in node-wise. Unfortunately, this unified formula reveals two inherent drawbacks. Firstly, the topology and node attribute are not reciprocal but biased. From employment, the topology information is repeatedly employed, while the node attribute is only used once. From parameterization perspective, the node attribute is parameterized with highly expressive MLPs, while topology is not. Secondly, the graph topology can not be fully explored. Only the local pairwise relation is explored, but the mesoscopic community structure, which is one of the most prominent characteristics of networks, is ignored. To alleviate these issues, this paper proposes the Graph Reciprocal Network (GRN) by treating node attribute and topology reciprocal. Firstly, it is illustrated that the node can be regarded and utilized as another kind of attribute. Secondly, a novel node representation scheme is proposed from the theory of Quadratic Networks, with a theoretical guarantee of the fine-grained element-wise product of the representations of the topology and attribute. Extensive experiments demonstrate the superior performance and robustness of the proposed GRN.

An explicit network structure plays an important role when analyzing and understanding diffusion processes. In many scenarios, however, the interactions between nodes in an underlying network are unavailable. Although many methods for inferring a network structure from observed cascades have been proposed, they did not perceive the relationship between pairwise interactions in a cascade. Therefore, this paper proposes a Pairwise-interactions-based Bayesian Inference method (named PBI) to infer the underlying diffusion network structure. More specifically, to get more accurate inference results, we measure the weights of each candidate pairwise interaction in different cascades and add them to the likelihood of a contagion process. In addition, a pre-pruning work is introduced for candidate edges to further improve the inference efficiency. Experiments on synthetic and real-world networks show that PBI achieves significantly better results.

The rapid diffusion of hazardous information in large-flow-based social media causes great economic losses and potential threats to society. It is crucial to infer the inner information source as early as possible to prevent further losses. However, existing localization methods wait until all deployed sensors obtain propagation information before starting source inference within a network, and hence the best opportunity to control propagation is missed. In this paper, we propose a new localization strategy based on finite deployed sensors, named Greedy-coverage-based Rapid Source Localization (GRSL), to rapidly, flexibly and accurately infer the source in the early propagation stage of large-scale networks. There are two phases in GRSL. In the first phase, the Greedy-based Strategy (GS) greedily deploys sensors to rapidly achieve wide area coverage at a low cost. In the second phase, when a propagation event within a network is observed by a part of the sensors, the Inference Strategy (IS) with an earlier response mechanism begins executing the source inference task in an earlier small infected area. Comprehensive experiments with the SOTA methods demonstrate the superior performance and robustness of GRSL in various application scenarios.

Due to the simplicity, intuition and explanation, most Graph Neural Networks (GNNs) are proposed by following the pipeline of message passing. Although they achieve superior performances in many tasks, propagation-based GNNs possess three essential drawbacks. Firstly, the propagation tends to produce smooth effect, which meets the inductive bias of homophily, and causes two serious issues: over-smoothing issue and performance drop on networks with heterophily. Secondly, the propagations to each node are irrelevant, which prevents GNNs from modeling high-order relation, and cause the GNNs fragile to the attributes noises. Thirdly, propagation-based GNNs may be fragile to topology noise, since they heavily relay on propagation over the topology. Therefore, the propagation, as the key component of most GNNs, may be the essence of some serious issues in GNNs. To get to the root of these issue, this paper attempts to replace the propagation with a novel local operation. Quantitative experimental analysis reveals: 1) the existence of low-rank characteristic in the node attributes from ego-networks and 2) the performance improvement by reducing its rank. Motivated by this finding, this paper propose the Low-Rank GNNs, whose key component is the low-rank attribute matrix approximation in ego-network. The graph topology is employed to construct the ego-networks instead of message propagation, which is sensitive to topology noises. The proposed Low-Rank GNNs posses some attractive characteristics, including robust to topology and attribute noises, parameter-free and parallelizable. Experimental evaluations demonstrate the superior performance, robustness to noises and universality of the proposed Low-Rank GNNs.

Recently, deep learning techniques have yielded immense success on recommender systems. However, one weakness of most deep methods is that, users/items mutual semantic relationships, which are latent in the user-item interactions, are not distilled out explicitly. Moreover, most methods have been primarily focused on representation learning in euclidean geometry. Since recent studies have shown that the bipartite graph structure has the non-euclidean latent anatomy, euclidean embeddings may suffer from a certain degree of distortion. In this work, we presentHyperbolicNeuralCollaborativeRecommender (HNCR), a deep hyperbolic representation learning method that exploits mutual semantic relationships among users/items for collaborative filtering tasks. HNCR first introduces a neighbor construction strategy to build user and item semantic neighborhoods. Then HNCR develops a framework based on deep learning and hyperbolic geometry to integrate constructed neighborhoods into recommendation. To evaluate our method, we conduct experiments on the four datasets. Experimental results show the superiority of HNCR compared with its euclidean counterpart and state-of-the-art recommendation baselines. The results also indicate that hyperbolic representations can reflect meaningful data insights.

Spectral clustering is a vital clustering method and has been widely applied for data analysis and pattern reorganization. A routine of solving spectral clustering problem consists of two successive stages: (1) solving a relaxed continuous optimization problem to obtain a real-valued indicator solution (2) transform the real-valued indicator into a 0-1 discrete one as the final clustering result. However, we may lose the optimal solution with such a two-stage process. Besides, the spectral clustering has a high time complexity which limits the analysis of large-scale data. To alleviate these problems, this paper proposes an efficient spectral clustering framework that computes spectral embedding and improved spectral rotation simultaneously (SE-ISR). In addition, we also provide a parameter-free method (SE-ISR-PF) to automatically choose the trade-off parameter. Furthermore, with an anchor-based similarity matrix construction, it is scalable to large-scale data. An effective algorithm with a strict convergence proof is provided to solve the corresponding optimization problem. Experimental results on several benchmark datasets demonstrate that the proposed algorithm outperforms the state-of-art methods.

Graph-based semi-supervised learning, which further utilizes graph structure behind samples for boosting semi-supervised learning, gains convincing results in several machine learning tasks. Nevertheless, existing graph-based methods have shortcomings from two aspects. On the one hand, many of them concentrate on improving label propagation over the constructed graph through time-saving methods, e.g. path searching, without giving insights on constructing a proper graph accommodated to samples. On the other hand, some models are only devoted to constructing the appropriate graph resulting in a two-stage procedure, which may incur a suboptimal scenario. In this paper, we develop a joint learning method that considers both bipartite graph construction and label propagation simultaneously. With this configuration, the constructed graph is constantly adjusted by the smoothness term in the objective as the algorithm proceeds. The time complexity of our method gets significant improvement compared with traditional graph-based methods, and the experimental results on one synthetic dataset and several real-world benchmarks demonstrate the effectiveness and scalability of our proposed method.

Recently, the rapid diffusion of malicious information in online social networks causes great harm to our society. Therefore, it is of great significance to localize diffusion sources as early as possible to stem the spread of malicious information. This paper proposes a novel sensor-based method, called greedy full-order neighbor localization (denoted as GFNL), to solve this problem under a low infection propagation in line with the real world. More specifically, GFNL includes two main components, i.e., the greedy-based sensor deployment strategy (DS) and direction-path-based source estimation strategy (ES). In more detail, to ensure sensors can observe a propagation information as early as possible, a set of sensors is deployed in a network to minimize the geodesic distance (i.e., the distance of the shortest path) between the candidate set and the sensor set based on DS. Then when a fraction of sensors observe a propagation, ES infers the source based on the idea that the distance of the actual propagation path is proportional to the observed time. Compared with some state-of-the-art methods, comprehensive experiments have proved the superiority and robustness of our proposed GFNL.

The design of fusion engines is a subject of great importance in a variety of fields. In this paper, we focus on the problem of linear fusion at the feature level for multiple signal matrices with noises, with the features being extremal eigenvectors. When given multiple similarity matrices, the objective is to find an estimate of the latent signal eigenspace. The concentration result for the inner product of features from different matrix samples is developed, utilizing the random matrix theory. Based on of the theoretical results, we proposed an efficient algorithm,EigFuse, to solve the constrained data-driven optimization problem with different level of noises. Our method is of high efficiency by comparing it with state-of-the-art baseline approaches with multiple noise levels. Comprehensive experiments on several synthetic as well as real-life networks demonstrate our method’s superior performance.

Community structure division is a crucial problem in the field of network data analysis. Algorithms based on Markov chains are easy to use and provide promising solutions for community detection. In a Markov chain-based algorithm (i.e., MCL), a flow distribution matrix and a transition matrix are used to describe stochastic flows and transition probabilities, respectively, on a network. The dynamic interaction process between stochastic flows and transition probabilities in MCLs is manifested through an iterative process of updating the abovementioned two matrices. As one of the key mechanisms of MCLs, such a dynamic process for increasing the inhomogeneity directly affects the accuracy and computational cost of MCL-based methods. Inspired by a kind of positive feedback interaction of a dendritic network of tube-like amoeba cell pseudopodia (named thePhysarumforaging network), aPhysarum-inspired relationship among vertices is proposed to enhance the transition probability in the dynamic process of MCL-based community detection algorithms. Specifically, the proposed hybrid community detection algorithm can adaptively search for a better combination of parameters based on a genetic algorithm. Some experiments are carried out on both static and dynamic networks. The results show that the uniquePhysaruminspired algorithm achieved better computational efficiency and detection performance than other algorithms.

Dimensionality reduction plays a significant role in high-dimensional data processing, and Linear Discriminant Analysis (LDA) is a widely used supervised dimensionality reduction approach. However, a major drawback of LDA is that it is incapable of extracting the local structure information, which is crucial for handling multimodal data. In this article, we propose a novel supervised dimensionality reduction method named Adaptive Local Linear Discriminant Analysis (ALLDA), which adaptively learns a k -nearest neighbors graph from data themselves to extract the local connectivity of data. Furthermore, the original high-dimensional data usually contains noisy and redundant features, which has a negative impact on the evaluation of neighborships and degrades the subsequent classification performance. To address this issue, our method learns the similarity matrix and updates the subspace simultaneously so that the neighborships can be evaluated in the optimal subspaces where the noises have been removed. Through the optimal graph embedding, the underlying sub-manifolds of data in intra-class can be extracted precisely. Meanwhile, an efficient iterative optimization algorithm is proposed to solve the minimization problem. Promising experimental results on synthetic and real-world datasets are provided to evaluate the effectiveness of proposed method.

In many real-world applications, we are often confronted with high dimensional data which are represented by various heterogeneous views. How to cluster this kind of data is still a challenging problem due to the curse of dimensionality and effectively integration of different views. To address this problem, we propose two parameter-free weighted multi-view projected clustering methods which perform structured graph learning and dimensionality reduction simultaneously. We can use the obtained structured graph directly to extract the clustering indicators, without performing other discretization procedures as previous graph-based clustering methods have to do. Moreover, two parameter-free strategies are adopted to learn an optimal weight for each view automatically, without introducing a regularization parameter as previous methods do. Extensive experiments on several public datasets demonstrate that the proposed methods outperform other state-of-the-art approaches and can be used more practically.

With the recent advances in information networks, the problem of identifying group structure or communities has received a significant amount of attention. Most of the existing principles of community detection or clustering mainly focus on either the topological structure of a network or the node attributes separately, while both of the two aspects provide valuable information to characterize the nature of communities. In this paper we combine the topological structure of a network as well as the content information of nodes in the task of detecting communities in information networks. Specifically, we treat a network as a dynamic system and consider its community structure as a consequence of interactions among nodes. To model the interactions we introduce the principle of content propagation and integrate the aspects of structure and content in a network naturally. We further describe the interactions among nodes in two different ways, including a linear model to approximate influence propagation, and modeling the interactions directly with random walk. Based on interaction modeling, the nature of communities is described by analyzing the stable status of the dynamic system. Extensive experimental results on benchmark datasets demonstrate the superiority of the proposed framework over the state of the art.

Multi-label learning deals with the problem where each training example is associated with a set of labels simultaneously, with the set of labels corresponding to multiple concepts or semantic meanings. Intuitively, the multiple labels are usually correlated in some semantic space while sharing the same input space. As a consequence, the multi-label learning process can be augmented significantly by exploiting the label correlations effectively. Most of the existing approaches share the limitations in that the label correlations are typically taken as prior knowledge, which may not depict the true dependencies among labels correctly, or they do not adequately address the issue of missing labels. In this paper, we propose an integrated framework that learns the correlations among labels while training the multi-label model simultaneously. Specifically, a low rank structure is adopted to capture the complex correlations among labels. In addition, we incorporate a supplementary label matrix which augments the possibly incomplete label matrix by exploiting the label correlations. An alternating algorithm is then developed to solve the optimization problem. Extensive experiments are conducted on a number of image and text data sets to demonstrate the effectiveness of the proposed approach.