The rapid growth of short video platforms has made short video fake news detection a critical security task. This task predicts news authenticity by leveraging multimodal information, including video frames, text, audio, and others. Previous works typically capture temporal cues only implicitly and suffer from passive coarse aggregation that favors global semantics, thereby obscuring subtle tampering traces. In addition, treating videos as isolated instances fails to exploit global correlations, which hinders the refinement of predictions for ambiguous cases. To address these limitations, we propose a two-stage framework with Temporal Reprogramming and Residual Graph Label Propagation (TR-GLP) for short video fake news detection. Specifically, we first employ a Temporal Reprogramming module in Stage 1 to learn temporal pattern prototypes as a reference basis for video dynamics and re-express keyframe features via cross-attention over these prototypes, where prototype attention activations reveal prototype-inconsistent temporal cues and support fine-grained forgery detection. Subsequently, we utilize a Residual Graph Label Propagation Network in Stage 2 to propagate supervision from labeled to unlabeled samples over the graph, and the residual design aggregates global context while preserving instance-level features. Experiments on FakeSV and FakeTT indicate that TR-GLP delivers superior performance compared with representative baseline methods.

The exponential rise of social media videos necessitates accurate detection of hate speech targeting protected attributes. However, existing multimodal approaches are hindered by two critical limitations: symmetric fusion, which induces modal competition and sensitivity to visual noise, and instance-based retrieval, which suffers from semantic ambiguity and high computational overhead due to reliance on raw data. To address these challenges, we propose the Multimodal Pattern Bank-guided Hierarchical Complementary Gating (HCG-MPB) framework. Specifically, we introduce a Hierarchical Complementary Gating (HCG) mechanism. It initially utilizes text as a semantic anchor to establish a stable context, and subsequently generates dynamic gating weights to selectively integrate audio-visual features, thereby effectively suppressing noise while preserving complementary information. Furthermore, to address semantic and efficiency challenges, we propose the Multimodal Pattern Bank (MPB). Instead of retrieving from vast raw instances, MPB leverages Large Language Models (LLMs) to distill extensive training samples into a compact set of interpretable prototypes. This approach significantly minimizes the storage footprint and retrieval latency, providing robust semantic guidance without the computational burden of traditional methods. Experiments on two public datasets demonstrate that HCG-MPB achieves excellent performance in both detection accuracy and efficiency.

Graph neural networks (GNNs) have shown promise in integrating protein-protein interaction (PPI) networks for identifying cancer genes in recent studies. However, due to the insufficient modeling of the biological information in PPI networks, more faithfully depiction of complex protein interaction patterns for cancer genes within the graph structure remains largely unexplored. This study takes a pioneering step toward bridging biological anomalies in protein interactions caused by cancer genes to statistical graph anomaly. We find a unique graph anomaly exhibited by cancer genes, namely weight heterogeneity, which manifests as significantly higher variance in edge weights of cancer gene nodes within the graph. Additionally, from the spectral perspective, we demonstrate that the weight heterogeneity could lead to the "flattening out" of spectral energy, with a concentration towards the extremes of the spectrum. Building on these insights, we propose the HIerarchical-Perspective Graph Neural Network (HIPGNN) that not only determines spectral energy distribution variations on the spectral perspective, but also perceives detailed protein interaction context on the spatial perspective. Extensive experiments are conducted on two reprocessed datasets STRINGdb and CPDB, and the experimental results demonstrate the superiority of HIPGNN.

With the rapid growth of the internet and digital economy, fraudulent activities have grown more sophisticated, leading to substantial social and financial costs. Graph-based fraud detection provides an effective framework by modeling entities as nodes and interactions as edges. However, the prevalent heterophily (target nodes exhibit distinct labels from neighbors) and heterogeneity (multi-typed nodes/edges) in fraud graphs cause traditional graph neural networks to lose critical anomaly signals during message passing. Existing methods handle heterophily and heterogeneity in isolation: heterophily mitigation relies on static grouping strategies, while heterogeneity handling predominantly adopts adaptive weighting mechanisms. Yet neither systematically models their synergistic effects, limiting hierarchical pattern capturing capabilities. This paper proposes HHGFD, a synergistic framework integrating reinforcement learning-driven dynamic grouping with hierarchical attention aggregation. We formulate grouping decisions as a Markov process through reinforcement learning, optimizing grouping boundaries via real-time feedback. Additionally, we introduce a hierarchical attention aggregation framework: node-level self-attention learns cross-type feature importance weights to emphasize fraud-relevant attributes, while relation-level attention dynamically allocates semantic weights for heterogeneous edges. Extensive experiments on four datasets demonstrate the effectiveness of our proposed method.

Graph-based fraud detection (GFD) has garnered increasing attention due to its effectiveness in identifying fraudsters within multimedia data such as online transactions, product reviews, or telephone voices. However, the prevalent in-distribution (ID) assumption significantly impedes the generalization of GFD approaches to out-of-distribution (OOD) scenarios, which is a pervasive challenge considering the dynamic nature of fraudulent activities. In this paper, we introduce the Heterophilic Graph Invariant Learning Framework (HGIF), a novel approach to bolster the OOD generalization of GFD. HGIF addresses two pivotal challenges: creating diverse virtual training environments and adapting to varying target distributions. Leveraging edge-aware augmentation, HGIF efficiently generates multiple virtual training environments characterized by generalized heterophily distributions, thereby facilitating robust generalization against fraud graphs with diverse heterophily degrees. Moreover, HGIF employs a shared dual-channel encoder with heterophilic graph contrastive learning, enabling the model to acquire stable high-pass and low-pass node representations during training. During the Test-time Training phase, the shared dual-channel encoder is flexibly fine-tuned to adapt to the test distribution through graph contrastive learning. Extensive experiments showcase HGIF's superior performance over existing methods in OOD generalization, setting a new benchmark for GFD in OOD scenarios.

The anonymity of online networks makes tackling fraud increasingly costly. Thanks to the superiority of graph representation learning, graph-based fraud detection has made significant progress in recent years. However, upgrading fraudulent strategies produces more advanced and difficult scams. One common strategy is synergistic camouflage —— combining multiple means to deceive others. Existing methods mostly investigate the differences between relations on individual frauds, that neglect the correlation among multi-relation fraudulent behaviors. In this paper, we design several statistics to validate the existence of synergistic camouflage of fraudsters by exploring the correlation among multi-relation interactions. From the perspective of multi-relation, we find two distinctive features of fraudulent behaviors, i.e., alienation and marginalization. Based on the finding, we propose COFRAUD, a correlation-aware fraud detection model, which innovatively incorporates synergistic camouflage into fraud detection. It captures the correlation among multi-relation fraudulent behaviors. Experimental results on two public datasets demonstrate that COFRAUD achieves significant improvements over state-of-the-art methods.

Collaborative fraud has become increasingly serious in telecom and social networks, but is hard to detect by traditional fraud detection methods. In this paper, we find a significant positive correlation between the increase of collaborative fraud and the degraded detection performance of traditional techniques, implying that those fraudsters that are difficult to detect with traditional methods are often collaborative in their fraudulent behavior. As we know, multiple objects may contact a single target object over a period of time. We define multiple objects with the same contact target as generalized objects, and their social behaviors can be combined and processed as the social behaviors of one object. We propose Fraud Detection Model based on Second-order and Collaborative Relationship Mining (COFD), exploring new research avenues for collaborative fraud detection. Our code and data are released at https://github.com/CatScarf/COFD-MM https://github.com/CatScarf/COFD-MM.

This paper proposes a novel problem of cross-regional friendship inference to solve the geographically restricted friends recommendation. Traditional approaches rely on a fundamental assumption that friends tend to be co-location, which is unrealistic for inferring friendship across regions. By reviewing a large-scale Location-based Social Networks (LBSNs) dataset, we spot that cross-regional users are more likely to form a friendship when their mobility neighbors are of high similarity. To this end, we propose Category-Aware Multi-Bipartite Graph Embedding (CMGE for short) for cross-regional friendship inference. We first utilize multi-bipartite graph embedding to capture users’ Point of Interest (POI) neighbor similarity and activity category similarity simultaneously, then the contributions of each POI and category are learned by a category-aware heterogeneous graph attention network. Experiments on the real-world LBSNs datasets demonstrate that CMGE outperforms state-of-the-art baselines.

Network alignment is the matching of two networks with corresponding nodes that belong to the same user or entity. The most common application is to analyze which accounts belong to the same user in two social networks. Most of existing techniques rely on matrix factorization so that they cannot be scaled to large-scale networks, are constrained by strict constraints, and cannot learn node embedding without a training set. In this paper, we propose an unsupervised network alignment model based on multi-level graph attention networks. The model uses multi-level graph attention network to learn the embedded representation of nodes, satisfying attribute and structure constraints of alignment. Augmented learning process is proposed to simulate attribute noise and structural noise to improve adaptability of the model. Extensive experiments on real datasets show that the proposed model performs better than the state-of-the-art network alignment model. We also demonstrate the robustness of the proposed model.