Restaurants, as small hospitality businesses, are inherently vulnerable, making accurate survival prediction crucial. Previous studies have demonstrated the significance of user reviews and incorporated diverse review?derived factors, yet they have largely overlooked the large?scale network formed by user–restaurant interactions. How restaurant survival is influenced by the review network remains insufficiently explored. To fill this gap, leveraging network embeddedness theory, we statistically analyze the impact of two dimensions of embeddedness, structural and positional, on each restaurant's survival. Utilizing two real-world review datasets, the newly curated OpenRice and the well-established Yelp, our results reveal that a restaurant's network embeddedness and its temporal evolution positively correlate with its survival. Building on this insight, we propose a Dynamic Embeddedness-aware Graph Neural Network, DyE-GNN, for restaurant survival prediction. DyE-GNN not only explicitly integrates network embeddedness theory to guide the model design but also leverages domain knowledge to enable robust adaptability. Extensive experiments on both datasets confirm the superiority of DyE-GNN, underscoring the importance of network embeddedness attention, temporal dynamics, and survival knowledge of peer restaurants. Visualizations further demonstrate that network embeddedness facilitates the identification of at-risk restaurants at the network margin.

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.

Graph neural networks (GNNs) encounter significant computational challenges when handling large-scale graphs, which severely restricts their efficacy across diverse applications. To address this limitation, graph condensation has emerged as a promising technique, which constructs a small synthetic graph for efficiently training GNNs while retaining performance. However, due to the topology structure among nodes, graph condensation is limited to condensing only the observed training nodes and their corresponding structure, thus lacking the ability to effectively handle the unseen data. Consequently, the original large graph is still required in the inference stage to perform message passing to inductive nodes, resulting in substantial computational demands. To overcome this issue, we propose mapping-aware graph condensation (MCond), explicitly learning the one-to-many node mapping from original nodes to synthetic nodes to seamlessly integrate new nodes into the synthetic graph for inductive representation learning. This enables direct information propagation on the synthetic graph, which is much more efficient than on the original large graph. Specifically, MCond employs an alternating optimization scheme with innovative loss terms from transductive and inductive perspectives, facilitating the mutual promotion between graph condensation and node mapping learning. Extensive experiments demonstrate the efficacy of our approach in inductive inference. On the Reddit dataset, MCond achieves up to 121.5× inference speedup and 55.9× reduction in storage requirements compared with counterparts based on the original graph.

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.

Social relationship mining benefits many applications such as leadership analysis and advisor recommendation. Existing methods focus on mining user relationships only from the perspective of user-level. To our knowledge, from this perspective, representing the user interactions by edges is not sufficient for the complex information about interactions between users. In addition, mining users' relationship independently ignores the propagation of social interaction across networks. In this paper, we investigate social relationship mining from a new perspective of interaction-level. We propose an Interaction Graph Propagating(IGP) model which constructs an interaction graph. It not only captures the user interaction information as the union but also exploits the propagation between user interactions. In particular, we utilize the graph attention mechanism to distinguish the contributions of each neighbor union. Experimental results on several public datasets demonstrate that IGP achieves significant improvements over state-of-the-art methods.

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.

Community search is a method of finding a com-munity closely related to a query node. The latest influence community search considers both the structural cohesion of the community and the influence between nodes. It sets the influence threshold to constrain the output community. However, artificially setting the influence threshold makes the output community too large or too small, which leads to low accuracy of the output community. In order to avoid the low accuracy of community search caused by artificially setting influence thresh-old constraints, this paper studies the community search problem based on community influence score. In this paper, an influence-truss community (ITC) model is proposed for community search by combining structural cohesion and community influence score. This model aims to obtain a connected subgraph in a social network containing the query node, which satisfies structural cohesion and satisfies the subgraph's maximum community in-fluence score. In order to obtain ITC, an effective pruning method is proposed, which strips other nodes far away from the query node. Then, the ITCS algorithm is designed, which firstly imposes structural cohesion constraints on query nodes. Then, the search community's influence scores are iteratively calculated until the community has the highest community influence score under the condition of meeting the structural cohesion. Experiments on real-world networks of different scales show that the community search accuracy index of ITCS is improved by about 20% compared with the traditional method.

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.