Recent studies have demonstrated that hypergraph neural networks (HGNNs) are susceptible to adversarial attacks. However, existing methods rely on the specific information mechanisms of target HGNNs, overlooking the common vulnerability caused by the significant differences in hyperedge pivotality along aggregation paths in most HGNNs, thereby limiting the transferability and effectiveness of attacks. In this paper, we present a novel framework, i.e., Transferable Hypergraph Attack via Injecting Nodes into Pivotal Hyperedges (TH-Attack), to address these limitations. Specifically, we design a hyperedge recognizer via pivotality assessment to obtain pivotal hyperedges within the aggregation paths of HGNNs. Furthermore, we introduce a feature inverter based on pivotal hyperedges, which generates malicious nodes by maximizing the semantic divergence between the generated features and the pivotal hyperedges features. Lastly, by injecting these malicious nodes into the pivotal hyperedges, TH-Attack improves the transferability and effectiveness of attacks. Extensive experiments are conducted on six authentic datasets to validate the effectiveness of TH-Attack and the corresponding superiority to state-of-the-art methods.

Most adversarial attacks on point clouds perturb a large number of points, causing widespread geometric changes and limiting applicability in real-world scenarios. While recent works explore sparse attacks by modifying only a few points, such approaches often struggle to maintain effectiveness due to the limited influence of individual perturbations. In this paper, we propose SCP, a sparse and cooperative perturbation framework that selects and leverages a compact subset of points whose joint perturbations produce amplified adversarial effects. Specifically, SCP identifies the subset where the misclassification loss is locally convex with respect to their joint perturbations, determined by checking the positive-definiteness of the corresponding Hessian block. The selected subset is then optimized to generate high-impact adversarial examples with minimal modifications. Extensive experiments show that SCP achieves 100% attack success rates, surpassing state-of-the-art sparse attacks, and delivers superior imperceptibility to dense attacks with far fewer modifications.

Recent studies have shown that Heterogeneous Graph Neural Networks (HetGNNs) are vulnerable to adversarial attacks. Existing methods rely on the gradient information of source models or surrogate models to generate perturbations, which limits the attack of transferability and effectiveness in real-world attack scenarios, while consuming time costs. In this paper, we propose a novel framework, i.e., Transferable Black-Box Injection Attack against Heterogeneous Graph Neural Networks (TBI-Attack), to address these challenges. Specifically, we introduce a voting-based key nodes recognizer based on different meta-paths to identify key nodes in relational subgraphs. Subsequently, we present a semantic-confused feature generator that leverages self-supervised learning to integrate neighborhood information from different relational subgraphs, generating malicious nodes with conflicting semantic features. Furthermore, malicious nodes are injected into various relational subgraphs, disrupting their specific semantic functionality and systematically impairing the message-passing process of HetGNNs. Extensive experiments are conducted on four authentic datasets to validate the effectiveness and transferability of TBI-Attack and the corresponding superiority to state-of-the-art methods.

While multi-label learning has shown excellent performance in False Data Injection Attack (FDIA) locational detection, it has also exposed some potential security risks and vulnerabilities. However, unlike the image domain, the vulnerabilities of multi-label learning in the field of power grid have just received attention and urgently need to be explored and addressed. In this paper, to achieve a better understanding for the security risks of deep learning-based multi-label FDIA detectors, we propose two Alternating Direction Method of Multipliers (ADMM) based adversarial attacks, which are applicable to two different scenarios. The proposed two ADMM-based attacks aim to reduce additional attack costs while seeking suitable adversarial perturbations, making the attacks more realistic and feasible. The experimental results verify the effectiveness of the proposed ADMM-based attacks, making noteworthy strides in fostering a profound comprehension of the vulnerabilities in the unique field of deep multi-label learning for power systems.

This paper studies the network dismantling (ND) problem and aims to develop more effective models and approaches to cope with it, such that a given network can be dismantled by a set of vital nodes of minimum size. To achieve that, we propose a three-phase framework—the Percolation coarsening, Belief propagation dismantling, and Fragmentation optimization fine-tuning (PBF) framework—consisting of PBF-I, PBF-II, and PBF-III, where we contribute three new and one improved algorithms. In particular, PBF-I studies strategies to effectively coarsen the studied network via the merger of less influential nodes, such that the computational efficiency of the follow-up PBF-II phase can be maximized. PBF-II considers the superiority of the belief propagation (BP) algorithm in the ND problem and proposes an improved BP to identify vital nodes from the coarse-grained network, which particularly focuses on the largest connected component and obtains the vital nodes from a filtered candidate set. In addition, PBF-III presents fine-tuning strategies to further improve the quality of solutions obtained in PBF-II. The effectiveness of the proposed framework is validated on over 10 empirical networks in regard to varied circumstances. Our results show that the developed framework can obtain dismantling node sets of much smaller sizes compared to the state-of-the-art in almost all cases. Meanwhile, our framework is also more effective, efficient, and stable compared to existing methods, and is capable of tackling the ND problem in extremely large networks. We are convinced that the model and methodology introduced in this paper could be applied to many applications, such as the robustness and resilience analysis of network-structural infrastructures, the suppression of epidemics, and the containment of misinformation on social networks. The source code of the proposed PBF framework will be made publicly available upon acceptance of the manuscript.

Recent studies have shown that Hypergraph Neural Networks (HGNNs) are vulnerable to adversarial attacks. Existing approaches focus on hypergraph modification attacks guided by gradients, overlooking node spanning in the hypergraph and the group identity of hyperedges, thereby resulting in limited attack performance and detectable attacks. In this manuscript, we present a novel framework, i.e., Hypergraph Attacks via Injecting Homogeneous Nodes into Elite Hyperedges (IE-Attack), to tackle these challenges. Initially, utilizing the node spanning in the hypergraph, we propose the elite hyperedges sampler to identify hyperedges to be injected. Subsequently, a node generator utilizing Kernel Density Estimation (KDE) is proposed to generate the homogeneous node with the group identity of hyperedges. Finally, by injecting the homogeneous node into elite hyperedges, IE-Attack improves the attack performance and enhances the imperceptibility of attacks. Extensive experiments are conducted on five authentic datasets to validate the effectiveness of IE-Attack and the corresponding superiority to state-of-the-art methods.

Adversarial attacks on point clouds are crucial for assessing and improving the adversarial robustness of 3D deep learning models. Traditional solutions strictly limit point displacement during attacks, making it challenging to balance imperceptibility with adversarial effectiveness. In this paper, we attribute the inadequate imperceptibility of adversarial attacks on point clouds to deviations from the underlying surface. To address this, we introduce a novel point-to-surface (P2S) field that adjusts adversarial perturbation directions by dragging points back to their original underlying surface. Specifically, we use a denoising network to learn the gradient field of the logarithmic density function encoding the shape’s surface, and apply a distance-aware adjustment to perturbation directions during attacks, thereby enhancing imperceptibility. Extensive experiments show that adversarial attacks guided by our P2S field are more imperceptible, outperforming state-of-the-art methods.

Population graphs are crucial for assessing clinical risk and enhancing the accuracy of Autism Spectrum Disorder (ASD) detection. Nevertheless, the current population graph construction overlooks the balance between biological signals and clinical manifestations, leading to relationship deviation within the population graph and poor detection performance. To address this challenge, we propose a novel approach for ASD Detection in Population Graphs (PopuDet) via Micro-macro Relationship Construction (MmRC) and Multi-feature Fusion (MF). Specifically, our method utilizes the MmRC module to construct a multi-scale population graph balancing the relationships between biological signals synchrony and clinical subtype groups. Subsequently, the MF module learns high-level graph representations at different scales for adaptive fusion to achieve precise ASD detection. Extensive experiments validate the efficiency of PopuDet, highlighting its superior performance over current state-of-the-art methods. Our source code is available at https://github.com/xuting99/PopuDet.

Source detection on graphs has demonstrated high efficacy in identifying rumor origins. Despite advances in machine learning-based methods, many fail to capture intrinsic dynamics of rumor propagation. In this work, we present SourceDetMamba: A Graph-aware State Space Model for Source Detection in Sequential Hypergraphs, which harnesses the recent success of the state space model Mamba, known for its superior global modeling capabilities and computational efficiency, to address this challenge. Specifically, we first employ hypergraphs to model high-order interactions within social networks. Subsequently, temporal network snapshots generated during the propagation process are sequentially fed in reverse order into Mamba to infer underlying propagation dynamics. Finally, to empower the sequential model to effectively capture propagation patterns while integrating structural information, we propose a novel graph-aware state update mechanism, wherein the state of each node is propagated and refined by both temporal dependencies and topological context. Extensive evaluations on eight datasets demonstrate that SourceDetMamba consistently outperforms state-of-the-art approaches.

Hypergraphs offer superior modeling capabilities for social networks, particularly in capturing group phenomena that extend beyond pairwise interactions in rumor propagation. Existing approaches in rumor source detection predominantly focus on dyadic interactions, which inadequately address the complexity of more intricate relational structures. In this study, we present a novel approach for Source Detection in Hypergraphs (HyperDet) via Interactive Relationship Construction and Feature-rich Attention Fusion. Specifically, our methodology employs an Interactive Relationship Construction module to accurately model both the static topology and dynamic interactions among users, followed by the Feature-rich Attention Fusion module, which autonomously learns node features and discriminates between nodes using a self-attention mechanism, thereby effectively learning node representations under the framework of accurately modeled higher-order relationships. Extensive experimental validation confirms the efficacy of our HyperDet approach, showcasing its superiority relative to current state-of-the-art methods.

Deep neural networks (DNNs) often exhibit overconfidence when encountering out-of-distribution (OOD) samples, posing significant challenges for deployment. Since DNNs are trained on in-distribution (ID) datasets, the information flow of ID samples through DNNs inevitably differs from that of OOD samples. In this paper, we propose an Entropy-based Out-Of-distribution Detection (EOOD) framework. EOOD first identifies specific block where the information flow differences between ID and OOD samples are more pronounced, using both ID and pseudo-OOD samples. It then calculates the conditional entropy on the selected block as the OOD confidence score. Comprehensive experiments conducted across various ID and OOD settings demonstrate the effectiveness of EOOD in OOD detection and its superiority over state-of-the-art methods.

In recent years, the rampant spread of misinformation on social media has made accurate detection of multimodal fake news a critical research focus. However, previous research has not adequately understood the semantics of images, and models struggle to discern news authenticity with limited textual information. Meanwhile, treating all emotional types of news uniformly without tailored approaches further leads to performance degradation. Therefore, we propose a novel Knowledge Augmentation and Emotion Guidance Network (KEN). On the one hand, we effectively leverage LVLM's powerful semantic understanding and extensive world knowledge. For images, the generated captions provide a comprehensive understanding of image content and scenes, while for text, the retrieved evidence helps break the information silos caused by the closed and limited text and context. On the other hand, we consider inter-class differences between different emotional types of news through balanced learning, achieving fine-grained modeling of the relationship between emotional types and authenticity. Extensive experiments on two real-world datasets demonstrate the superiority of our KEN.

Previous studies on multimodal fake news detection mainly focus on the alignment and integration of cross-modal features, as well as the application of text-image consistency. However, they overlook the semantic enhancement effects of large multimodal models and pay little attention to the emotional features of news. In addition, people find that fake news is more inclined to contain negative emotions than real ones. Therefore, we propose a novel Semantic Enhancement and Emotional Reasoning (SEER) Network for multimodal fake news detection. We generate summarized captions for image semantic understanding and utilize the products of large multimodal models for semantic enhancement. Inspired by the perceived relationship between news authenticity and emotional tendencies, we propose an expert emotional reasoning module that simulates real-life scenarios to optimize emotional features and infer the authenticity of news. Extensive experiments on two real-world datasets demonstrate the superiority of our SEER over state-of-the-art baselines.

With the rapid growth of global air traffic, 4D-flight trajectory prediction (4D-FTP) using deep learning (DL) methods has become essential for applications, such as flight delay prediction, fuel consumption analysis, and traffic management. However, the adversarial attacks pose significant security threats to DL-based 4D-FTP systems reliant on automatic dependent surveillance-broadcast (ADS-B) sensors. Furthermore, existing vulnerabilities in time-series prediction (TSP) models for 4D-FTP remain underexplored due to the lack of effective and stealthy attack methods. To address this gap, we propose the dynamic quasi-hyperbolic momentum (QHM) iterative attack with small perturbation (DQM-Attack). This method leverages QHM and dynamic step sizes to optimize gradient utilization in 4D-FTP models. In addition, attack stealth is enhanced through the manifold smooth module (MSM) and sparse smooth reinforcement learning (SS-RL). Experimental results demonstrate that DQM-Attack effectively disrupts predictions with minimal perturbations across four state-of-the-art TSP models. This study appears to be the first to address stealthy adversarial attacks on 4D-FTP systems, revealing a critical security vulnerability in air traffic management (ATM).

In the past era of explosive data growth, how to deal with large-scale, unlabeled remote sensing images (RSIs) has become a concern. Due to the lack of data labels, unsupervised methods are usually used to deal with them. As one of the best unsupervised algorithms, spectral clustering (SC) is a very effective data processing and analysis technology. However, the scalability of SC affects its development in the era of big data. Deep network technology has developed rapidly in the past, and it is often used to solve the problem of out-of-sample expansion (OOSE) that traditional machine learning cannot solve. However, because RSI is usually high-dimensional data, it is easy to cause dimension explosion in deep networks. The main motivation of this work is to solve the above problems. We have designed a new algorithm called deep SC with projected adaptive feature selection (DSCFS), which benefits from deep learning theory and feature projection technology. We use a neural network to map RSI data and then further process the data through regularization embedding. At the same time, adaptive feature projection is applied to extract the main features of RSI, and the loss feedback network is calculated through these two steps. A large number of experiments show that the performance of our proposed method is better than other mainstream methods.

Rumor source detection in structurally incomplete networks holds significant practical importance. Existing methods predominantly assume a complete network structure information; furthermore, they often neglect the issue of resource consumption, i.e., sensor deployment. In this paper, we propose an efficient source detection approach in incomplete networks via propagation-aware Sensor Deployment and time stamp-guided Source Approaching (SDSA) to tackle these challenges. Specifically, during the sensor deployment phase, we employ quality-guaranteed Monte Carlo propagation simulations coupled with a greedy strategy to achieve maximum coverage with minimal sensors. In the source detection phase, for the structurally incomplete network snapshots, we first attempt edge reconnection from the sensor with the earliest timestamp, followed by posterior maximization Bayesian estimation for source identification. Extensive experiments demonstrate the effectiveness of SDSA and its superiority over state-of-the-art methods. The code has been made publicly available athttps://github.com/cheng-le/SDSA.

Source detection in graphs has demonstrated robust efficacy in the domain of rumor source identification. Although recent solutions have enhanced performance by leveraging deep neural networks, they often require complete user data. In this paper, we address a more challenging task, rumor source detection with incomplete user data, and propose a novel framework, i.e., Source Detection in Graphs with Incomplete Nodes via Positional Encoding and Attentive Fusion (GIN-SD), to tackle this challenge. Specifically, our approach utilizes a positional embedding module to distinguish nodes that are incomplete and employs a self-attention mechanism to focus on nodes with greater information transmission capacity. To mitigate the prediction bias caused by the significant disparity between the numbers of source and non-source nodes, we also introduce a class-balancing mechanism. Extensive experiments validate the effectiveness of GIN-SD and its superiority to state-of-the-art methods.

With the rise of social media, the spread of fake news has become a significant concern, potentially misleading public perceptions and impacting social stability. Although deep learning methods like CNNs, RNNs, and Transformer-based models like BERT have enhanced fake news detection. However, they primarily focus on content and do not consider social context during news propagation. Graph-based techniques have incorporated the social context but are limited by the need for large labeled datasets. To address these challenges, this paper introduces GAMC, an unsupervised fake news detection technique using the Graph Autoencoder with Masking and Contrastive learning. By leveraging both the context and content of news propagation as self-supervised signals, our method reduces the dependency on labeled datasets. Specifically, GAMC begins by applying data augmentation to the original news propagation graphs. Subsequently, these augmented graphs are encoded using a graph encoder and subsequently reconstructed via a graph decoder. Finally, a composite loss function that encompasses both reconstruction error and contrastive loss is designed. Firstly, it ensures the model can effectively capture the latent features, based on minimizing the discrepancy between reconstructed and original graph representations. Secondly, it aligns the representations of augmented graphs that originate from the same source. Experiments on the real-world dataset validate the effectiveness of our method.

The precise detection of mild cognitive impairment (MCI) is of significant importance in preventing the deterioration of patients in a timely manner. Although hypergraphs have enhanced performance by learning and analyzing brain networks, they often only depend on vector distances between features at a single scale to infer interactions. In this paper, we deal with a more arduous challenge, hypergraph modelling with synchronization between brain regions, and design a novel framework, i.e., A Multi-scale Hypergraph Network for MCI Detection via Synchronous and Attentive Fusion (MHSA), to tackle this challenge. Specifically, our approach employs the Phase-Locking Value (PLV) to calculate the phase synchronization relationship in the spectrum domain of regions of interest (ROIs) and designs a multi-scale feature fusion mechanism to integrate dynamic connectivity features of functional magnetic resonance imaging (fMRI) from both the temporal and spectrum domains. To evaluate and op-timize the direct contribution of each ROI to phase synchronization in the temporal domain, we structure the PLV coefficients dynamically adjust strategy, and the dynamic hypergraph is modelled based on a comprehensive temporal-spectrum fusion matrix. Experiments on the real-world dataset indicate the effectiveness of our strategy. The code is available at https://github.com/Jia-Weiming/MHSA.

Deep neural networks (DNNs) often display overconfidence when encountering out-of-distribution (OOD) samples, posing significant challenges in real-world applications. Capitalizing on the observation that responses on convolutional kernels are generally more pronounced for in-distribution (ID) samples than for OOD ones, this paper proposes the COnvolutional REsponse-based Score (CORES) to exploit these discrepancies for OOD detection. Initially, CORES delves into the extremities of convolutional responses by considering both their magnitude and the frequency of significant values. Moreover, through backtracking from the most prominent predictions, CORES effectively pinpoints sample-relevant kernels across different layers. These kernels, which exhibit a strong correlation to input samples, are integral to CORES's OOD detection capability. Comprehensive experiments across various ID and OOD settings demonstrate CORES's effectiveness in OOD detection and its superiority to the state-of-the-art methods.

Online social networks are crucial for information acquisition nowadays, yet they are increasingly jeopardized by malicious attacks from social bots. This underscores the urgent need for robust social bot detection methods. To address the significant challenge posed by the imbalance in the number of human and bot users, we introduce a Graph Adversarial Augmentation-based method for social Bot Detection (GAA-BD) to improve detection efficacy. Our method incorporates a graph convolutional neural network as its core architecture and enhances it with adversarial augmentation applied to both the dataset and the training process. By strategically generating synthetic samples and employing targeted adversarial training, our approach effectively resolves sample size imbalances and bolsters model robustness. Comprehensive experimental results demonstrate that our proposed method outperforms existing baselines, establishing its effectiveness in combatting social bot infiltration in online networks.

Reparameterization techniques have demonstrated their efficacy in improving the efficiency of deep neural networks. However, their application has been largely confined to single-input network structures, leaving multi-input ones, commonly encountered in real-world applications, largely unexplored. In this paper, we formulate reparameterization head (RepHead), the first framework designed to introduce reparameterization into multi-input neural networks. RepHead compresses multiple inputs into a single input and employs reconstruction operations to recover them, thereby transforming multi-input networks into single-input, multibranch architectures, thereby enabling the application of reparameterization. We demonstrate the usage of RepHead in both image and point cloud domains. Extensive experimental results validate that the integration of RepHead substantially reduces computational overhead and memory requirements while maintaining minimal performance loss.

Adversarial attacks on point clouds are crucial for assessing and improving the adversarial robustness of 3D deep learning models. Despite leveraging various geometric constraints, current adversarial attack strategies often suffer from inadequate imperceptibility. Given that adversarial perturbations tend to disrupt the inherent symmetry in objects, we recognize this disruption as the primary cause of the lack of imperceptibility in these attacks. In this paper, we introduce a novel framework, symmetry-aware imperceptible adversarial attacks on 3D point clouds (SymAttack), to address this issue. Our approach starts by identifying part- and patch-level symmetry elements, and grouping points based on semantic and Euclidean distances, respectively. During the adversarial attack iterations, we intentionally adjust the perturbation vectors on symmetric points relative to their symmetry plane. By preserving symmetry within the attack process, SymAttack significantly enhances imperceptibility. Extensive experiments validate the effectiveness of SymAttack in generating imperceptible adversarial point clouds, demonstrating its superiority over the state-of-the-art methods.

Recently, higher order patterns have played an important role in network structure analysis. The simplices in higher order patterns enrich dynamic network modeling and provide strong structural feature information for feature learning. However, the disorder dynamic network with simplex patterns has not been organized and divided according to time windows. Besides, existing methods do not make full use of the feature information to predict the simplex patterns with higher orders. To address these issues, we propose a simplex pattern prediction method based on dynamic higher order path convolutional networks. First, we divide the dynamic higher order datasets into different network structures under continuous-time windows, which possess complete time information. Second, feature extraction is performed on the network structure of continuous-time windows through higher order path convolutional networks. Subsequently, we embed time nodes into feature encoding and obtain feature representations of simplex patterns through feature fusion. The obtained feature representations of simplices are recognized by a simplex pattern discriminator to predict the simplex patterns at different moments. Finally, compared to other dynamic graph representation learning algorithms, our proposed algorithm has significantly improved its performance in predicting simplex patterns on five real dynamic higher order datasets.

Graph neural network (GNN) has achieved remarkable success in various graph learning tasks, such as node classification, link prediction, and graph classification. The key to the success of GNN lies in its effective structure information representation through neighboring aggregation. However, the attacker can easily perturb the aggregation process through injecting fake nodes, which reveals that GNN is vulnerable to the graph injection attack (GIA). Existing GIA methods primarily focus on damaging the classical feature aggregation process while overlooking the neighborhood aggregation process via label propagation. To bridge this gap, we propose the label-propagation-based global injection attack (LPGIA) which conducts the GIA on the node classification task. Specifically, we analyze the aggregation process from the perspective of label propagation and transform the GIA problem into a global injection label specificity attack problem. To solve this problem, LPGIA utilizes a label-propagation-based strategy to optimize the combinations of the nodes connected to the injected node. Then, LPGIA leverages the feature mapping to generate malicious features for injected nodes. In extensive experiments against representative GNNs, LPGIA outperforms the previous best-performing injection attack method in various datasets, demonstrating its superiority and transferability.

Defect recognition is an important task in intelligent manufacturing. Due to the subjectivity of human annotation, the collected defect data usually contains a lot of noise and unpredictable uncertainties, which have a great negative influence on defect recognition. It is a significant challenge to discover an effective defect recognition model with satisfactory uncertainty processing ability. A natural way is to automatically search for an efficient deep model, which can be realized by neural architecture search (NAS). To achieve this, we propose an efficient fuzzy NAS framework for defect recognition, where the searched architecture can effectively handle uncertain information from the given datasets. Specifically, we first design a fuzzy search space and the related encoding strategy for fuzzy NAS. Then, we propose a comparator-based evolutionary search approach, where an online end-to-end comparator is learned to directly determine the selection of candidate architectures from the evolutionary population. The comparator works in an end-to-end way and it transforms the complex ranking problem of evaluating architectures into a simple classification task, which overcomes the rank disorder issue suffered from traditional performance predictors. A series of experimental results demonstrate that the architecture with fewer #Params (1.22 M) search by fuzzy neural architecture search framework for defect recognition method achieves higher accuracy (92.26%) compared to the state-of-the-art results (i.e., DARTS-PV) on the ELPV dataset, as well as competitive results (accuracy = 76.4%, #Params = 1.04 M) on the CODEBRIM dataset. Experimental results show the effectiveness and efficiency of our proposed method in handling uncertain problems.

We study the containment of diffusion in a network immunization perspective, whose solution also plays fundamental roles in scenarios such as the inference of rumor sources and the control of malicious viral marketings. In general, the network immunization aims to suppress the giant connected component of a network by removing as fewer nodes as possible, so that the intervention of the transmission could be achieved by only a few resources. Here, rather than that and based on the fact that removing edges might be cheaper and more applicable in some scenarios, we investigate which group of edges whose removal could boost the performance of an immunization strategy more effectively. We consider both cases that the network topology is known and unknown, and thus two approaches are accordingly developed based on the Edge RelationShip (ERS) and Explosive Percolation over Partial (EPP) information. We evaluate the performance of ERS by comparing it with strategies based on the edge betweenness, the product of eigenvector centralities of the nodes connected by edges, the epidemic link equations, etc. Results on over 30 real networks show that ERS could effectively acquire far better solutions by much less computing time. We also demonstrate the performance of EPP in the circumstances of decentralized, centralized, and delayed cases. We find that the performance of EPP would be in a degree degraded by the uncertainty of inferences from individuals, inaccuracy of predictions, and delay of reactions. But in almost all cases, the developed approach can more effectively suppress a diffusion compared to the currently random strategy, especially when a tough restriction is needed or a combination with the acquaintance immunization is conducted.

The rapid development of social networks has given opportunities for rumors to disturb the order of society. However, due to the diversity and complexity of users and information dissemination dynamics, localizing the rumor sources in social networks is still a critical and crucial problem yet to be well solved. Recent years, although several methods have been proposed to attempt to solve this problem, they suffer from the contradiction between accuracy and model complexity. To detect information sources efficiently, this article propose a heuristic framework for sources detection (HFSD) in social networks via graph convolutional networks which handles three major challenges, including: 1) the diversity and complexity of users and information dissemination dynamics; 2) difficulty to detect multiple sources, especially without knowing the number of sources; and 3) the class imbalance problem caused by the large differences in sample size of sources and nonsources. Specifically, first, to counteract the diversity and complexity of users and information, different kinds of features of users and information are encoded in the raw feature vectors; second, to address multisource detection, we adopt a binary classification in the last layer of model, which is different from the n-classification methods that are always applied to single-source scenario; finally, to solve the class imbalance problem, we design a balance mechanism which offsets the differences in sample size between the sets of sources and nonsources. Extensive experiments conducted on 12 real-world datasets demonstrate that HFSD can handle problems mentioned above and outperforms than state-of-the-art algorithms significantly.

Exploring adversarial attacks on deep neural networks (DNNs) is crucial for assessing and enhancing their adversarial robustness. Among various attack types, hard-label attacks that rely only on predicted labels offer a practical approach. This paper focuses on the challenging task of hard-label attacks within an extremely limited query budget, which is a significant achievement rarely accomplished by existing methods. To tackle this, we propose an attack framework that leverages geometric information from previous perturbation directions to form triangles and employs a heuristic perpendicular trial to effectively utilize the intermediate directions. Extensive experiments validate the effectiveness of our approach under strict query constraints and demonstrate its superiority to the state-of-the-art methods.

Deep neural networks often exhibit the overconfidence issue when encountering out-of-distribution (OOD) samples. To address this, leveraging large-scale pre-trained models like CLIP has shown promise. While CLIP has the capability to encode a vast array of interconnected concepts, current OOD detection methods based on it primarily focus on ID categories and a limited set of OOD categories. In this paper, we propose a novel approach that harnesses the power of WordNet to fully exploit the rich knowledge encapsulated within CLIP, resulting in enhanced OOD detection performance. Our methodology involves constructing a word tree that includes both in-distribution (ID) words and a large set of semantically similar OOD words selected from WordNet. By matching a test image with the concepts of the words in the word tree using CLIP, we estimate the probability of the image being classified as either ID or OOD. Furthermore, we introduce a conditional random field model to effectively handle both the parent-child and the sibling-sibling conflicts in the concept matching results. Extensive experiments under various ID/OOD settings demonstrate the effectiveness of our approach and its superiority over state-of-the-art methods.

3D point cloud perception using deep neural networks (DNNs) has been a trend for various application scenarios. However, the black-box nature of DNNs will bring many hidden risks as in the 2D image field. In this paper, we present a preliminary evaluation on the out-of-distribution (OOD) overconfidence issue of deep point cloud classifiers, which has been proven to exist in deep 2D image classifiers, i.e., OOD inputs will lead to overconfident predictions on predefined categories. We also investigate whether a simple thresholding baseline and two modern OOD detection solutions can handle the issue by detecting OOD samples. Extensive experiments with four representative deep point cloud classifiers train/evaluate on different in/out-of-distribution point clouds validate the severity and knottiness of the OOD overconfidence issue. Our investigation will provide the groundwork for future studies on handling the OOD overconfidence issue of DNN classifiers for 3D point clouds.

Adversarial attacks have been successfully extended to the field of point clouds. Besides applying the common perturbation guided by the gradient, adversarial attacks on point clouds can be conducted by applying directional perturbations, e.g., along normal and along the tangent plane. In this article, we first investigate whether adversarial attacks with these two orthogonal directional perturbations are more imperceptible than that with the gradient-aware perturbation. Second, we investigate the deeper difference between adversarial attacks with these two directional perturbations, and whether they are applicable to the same scenarios. Third, based on the verification results that the above two directional perturbations have different sensitiveness to curvature, we devise a novel normal-tangent attack (NTA) framework with a hybrid directional perturbation scheme that adaptively chooses the direction according to the curvature of the local shape around the point. Extensive experiments on two publicly available data sets, e.g., ModelNet40 and ShapeNet Part, with classifiers in three representative networks, e.g., PointNet++, DGCNN, PointConv, validate the effectiveness of NTA, and the superiority to the state-of-the-art methods.

Deceptive reviews on social media and e-commerce websites are inflammatory and will significantly affect the judgment and purchase behavior of other users. At present, many researchers build models based on single text features to detect deceptive reviews. However, deceptive reviewers will deliberately imitate the text style of true reviews when writing reviews. At this time, these methods based on text features are not necessarily effective. What’s more, detection performance is limited because the category distribution is likely to be unbalanced in practice. In this work, to address these shortcomings, a deceptive review detection method based on multidimensional feature construction and ensemble feature selection is proposed. Our proposal constructs 3-D features including text feature, reviewer behavior feature, and deceptive score feature. In addition, to alleviate the impact of unbalanced category distribution, a data resampling algorithm is applied which incorporates random under-sampling (RUS) and Borderline-SMOTE algorithm. Furthermore, we integrate the results of different feature selection based on the Chi-square test, Information gain, and XGBoost feature importance. Our method addresses the limitation of single dimension features and can provide useful detection. Experimental results area under the curve (AUC, Macro Average Precision, and weighted F1-score) show that the proposed method performs well in the task of deceptive review detection on two Amazon datasets. Compared with other advanced methods, our method achieves additional performance gains in the case of poor text quality and datasets with unbalanced category distribution.

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.

Though several deep learning (DL) detectors have been proposed for the network attack detection and achieved high accuracy, they are computationally expensive and struggle to satisfy the real-time detection for high-speed networks. Recently, programmable switches exhibit a remarkable throughput efficiency on production networks, indicating a possible deployment of the timely detector. Therefore, we present Soter, a DL enhanced in-network framework for the accurate real-time detection. Soter consists of two phases. One is filtering packets by a rule-based decision tree running on the Tofino ASIC. The other is executing a well-designed lightweight neural network for the thorough inspection of the suspicious packets on the CPU. Experiments on the commodity switch demonstrate that Soter behaves stably in ten network scenarios of different traffic rates and fulfills per-flow detection in 0.03s. Moreover, Soter naturally adapts to the distributed deployment among multiple switches, guaranteeing a higher total throughput for large data centers and cloud networks.

The three-dimensional genome structure plays a key role in cellular function and gene regulation. Single-cell Hi-C (high-resolution chromosome conformation capture) technology can capture genome structure information at the cell level, which provides the opportunity to study how genome structure varies among different cell types. Recently, a few methods are well designed for single-cell Hi-C clustering. In this manuscript, we perform an in-depth benchmark study of available single-cell Hi-C data clustering methods to implement an evaluation system for multiple clustering frameworks based on both human and mouse datasets. We compare eight methods in terms of visualization and clustering performance. Performance is evaluated using four benchmark metrics including adjusted rand index, normalized mutual information, homogeneity and Fowlkes-Mallows index. Furthermore, we also evaluate the eight methods for the task of separating cells at different stages of the cell cycle based on single-cell Hi-C data.

Redundant systems, which usually consist of a number of same/similar components (or modules), have been used in various critical infrastructures to ensure the system's normal function. Usually, system reliability can be improved with the adoption of additional components or redundancies. Typically, two types are mainly included, i.e., majority voters and standby redundancies (referred to as SPARE gates for simplicity). Nevertheless, with the increment of redundancies, the consumed cost or space requirement also grows. This study considers a tradeoff between cost and reliability in order to pursue the cost-effective optimal design. The relationships between the total cost and corresponding parameters are discussed thoroughly. Besides, in order to determine the cost-effective design at the expense of a unit of cost, a revised evaluation standard is proposed (referred to as R per Cost). In this paper, we perform a cost-effective analysis of majority voters with different implementations; and we also perform the analyses of SPARE gates (here, a warm spare gate and a cold spare gate are mainly focused). For deriving corresponding total cost, algorithms are presented to predict the necessary failure time of components. In this line, cost-effective analyses of several case studies are performed.

A phased-mission system (PMS) usually consists of several nonoverlapping phases of tasks. All phases are required to be accomplished sequentially for a successful mission. Different features must be considered in the reliability evaluation of a PMS, including the dependence among the phases with respect to a common component and the different system topologies for the phases. To overcome the limitation of existing approaches, a stochastic computational approach is proposed for efficiently analyzing the reliability of a nonrepairable PMS. Stochastic logic models are proposed to analyze the common components in the different phases. In the stochastic analysis, the signal probabilities of the basic components are encoded as non-Bernoulli sequences of random permutations with fixed numbers of 1s and 0s. Thus, the proposed stochastic approach can be used to evaluate a PMS under any distribution. Based on the generated stochastic sequences for the basic components and the system topology, the failure probability of the PMS can be efficiently predicted. Several case studies are evaluated to show the accuracy and efficiency of the stochastic approach. Compared with a combinatorial analysis, the accuracy of the stochastic analysis varies with the length of the stochastic sequences. However, it is shown that the stochastic analysis is more efficient than a Monte Carlo simulation at the same execution complexity in the number of runs.

A redundant system usually consists of primary and standby modules. The so-called spare gate is extensively used to model the dynamic behavior of redundant systems in the application of dynamic fault trees (DFTs). Several methodologies have been proposed to evaluate the reliability of DFTs containing spare gates by computing the failure probability. However, either a complex analysis or significant simulation time are usually required by such an approach. Moreover, it is difficult to compute the failure probability of a system with component failures that are not exponentially distributed. Additionally, probabilistic common cause failures (PCCFs) have been widely reported, usually occurring in a statistically dependent manner. Failure to account for the effect of PCCFs overestimates the reliability of a DFT. In this paper, stochastic computational models are proposed for an efficient analysis of spare gates and PCCFs in a DFT. Using these models, a DFT with spare gates under PCCFs can be efficiently evaluated. In the proposed stochastic approach, a signal probability is encoded as a non-Bernoulli sequence of random permutations of fixed numbers of ones and zeros. The component's failure probability is not limited to an exponential distribution, thus this approach is applicable to a DFT analysis in a general case. Several case studies are evaluated to show the accuracy and efficiency of the proposed approach, compared to both an analytical approach and Monte Carlo (MC) simulation.

Reliability is fast becoming a major concern due to the nanometric scaling of CMOS technology. Accurate analytical approaches for the reliability evaluation of logic circuits, however, have a computational complexity that generally increases exponentially with circuit size. This makes intractable the reliability analysis of large circuits. This paper initially presents novel computational models based on stochastic computation; using these stochastic computational models (SCMs), a simulation-based analytical approach is then proposed for the reliability evaluation of logic circuits. In this approach, signal probabilities are encoded in the statistics of random binary bit streams and non-Bernoulli sequences of random permutations of binary bits are used for initial input and gate error probabilities. By leveraging the bit-wise dependencies of random binary streams, the proposed approach takes into account signal correlations and evaluates the joint reliability of multiple outputs. Therefore, it accurately determines the reliability of a circuit; its precision is only limited by the random fluctuations inherent in the stochastic sequences. Based on both simulation and analysis, the SCM approach takes advantages of ease in implementation and accuracy in evaluation. The use of non-Bernoulli sequences as initial inputs further increases the evaluation efficiency and accuracy compared to the conventional use of Bernoulli sequences, so the proposed stochastic approach is scalable for analyzing large circuits. It can further account for various fault models as well as calculating the soft error rate (SER). These results are supported by extensive simulations and detailed comparison with existing approaches.

Dynamic fault tree (DFT) analysis has been used to account for dynamic behaviors such as the sequence-dependent, functional-dependent, and priority relationships among the failures of basic events. Various methodologies have been developed to analyze a DFT; however, most methods require a complex analytical procedure or a significant simulation time for an accurate analysis. In this paper, a stochastic computational approach is proposed for an efficient analysis of the top event's failure probability in a DFT with priority AND (PAND) gates. A stochastic model is initially proposed for a two-input PAND gate, and a successive cascading model is then presented for a general multiple-input PAND gate. A stochastic approach using the proposed models provides an efficient analysis of a DFT compared to an accurate analysis or algebraic approach. The accuracy of a stochastic analysis increases with the length of random binary bit streams in stochastic computation. The use of non-Bernoulli sequences of random permutations of fixed counts of 1s and 0s as initial input events' probabilities makes the stochastic approach more efficient, and more accurate than Monte Carlo simulation. Non-exponential failure distributions and repeated events are readily handled by the stochastic approach. The accuracy, efficiency, and scalability of the stochastic approach are shown by several case studies of DFT analysis.