Fraud detection that aims to discern frauds from the majority of benigns has become an increasingly prominent research field. Recently, Graph Neural Networks (GNNs) have been widely applied in graph-based fraud detection due to their outstanding data analysis and mining capabilities. However, owing to the inherent homophily-heterophily mixture and class imbalance of fraud graphs, most GNNs with homophily assumption inevitably suffer from local abnormal signal loss during information propagation, posing significant challenges in situations where frauds are rare and valuable. To address the aforementioned issues, we present a novel dynamic neighborhood modeling via node-subgraph contrastive learning for graph-based fraud detection, dubbed DCL-GFD. Specifically, we first design a node abnormality estimation module from the perspective of feature, which analyses the likelihood of a node belonging to fraud or benign by comparing the feature similarity between the target node and its corresponding subgraph. We then present a dynamic neighborhood modeling mechanism guided by the abnormal probability of a node to adaptively group and aggregate neighborhood information. By this means, the target node can effectively aggregate the neighbor information from the perspective of fraud or benign, thereby preserving as much fraud characteristics that occupy minority population as possible. Extensive experiments across four real-world fraud detection datasets demonstrate the superiority and effectiveness of our proposed DCL-GFD over state-of-the-art baselines.

Heterogeneous information networks (HINs) are potent models of complex systems. In practice, many nodes in an HIN have their attributes unspecified, resulting in significant performance degradation for supervised and unsupervised representation learning. We developed an unsupervised heterogeneous graph contrastive learning approach for analyzing HINs with missing attributes (HGCA). HGCA adopts a contrastive learning strategy to unify attribute completion and representation learning in an unsupervised heterogeneous framework. To deal with a large number of missing attributes and the absence of labels in unsupervised scenarios, we proposed an augmented network to capture the semantic relations between nodes and attributes to achieve a fine-grained attribute completion. Extensive experiments on three large real-world HINs demonstrated the superiority of HGCA over several state-of-the-art methods. The results also showed that the complemented attributes by HGCA can improve the performance of existing HIN models.

Trust evaluation is critical for many applications such as cyber security, social communication, and recommender systems. Users and trust relationships among them can be seen as a graph. Graph neural networks (GNNs) show their powerful ability for analyzing graph-structural data. Very recently, existing work attempted to introduce the attributes and asymmetry of edges into GNNs for trust evaluation, while failed to capture some essential properties (e.g., the propagative and composable nature) of trust graphs. In this work, we propose a new GNN-based trust evaluation method named TrustGNN, which integrates smartly the propagative and composable nature of trust graphs into a GNN framework for better trust evaluation. Specifically, TrustGNN designs specific propagative patterns for different propagative processes of trust, and distinguishes the contribution of different propagative processes to create new trust. Thus, TrustGNN can learn comprehensive node embeddings and predict trust relationships based on these embeddings. Experiments on some widely-used real-world datasets indicate that TrustGNN significantly outperforms the state-of-the-art methods. We further perform analytical experiments to demonstrate the effectiveness of the key designs in TrustGNN.

Graph Convolutional Network (GCN) has shown remarkable potential of exploring graph representation. However, the GCN aggregating mechanism fails to generalize to networks with heterophily where most nodes have neighbors from different classes, which commonly exists in real-world networks. In order to make the propagation and aggregation mechanism of GCN suitable for both homophily and heterophily (or even their mixture), we introduce block modelling into the framework of GCN so that it can realize “block-guided classified aggregation”, and automatically learn the corresponding aggregation rules for neighbors of different classes. By incorporating block modelling into the aggregation process, GCN is able to automatically aggregate information from homophilic and heterophilic neighbors discriminately according to their homophily degree. We compared our algorithm with state-of-art methods which deal with the heterophily problem. Empirical results demonstrate the superiority of our new approach over existing methods in heterophilic datasets while maintaining a competitive performance in homophilic datasets.

Heterogeneous information networks (HINs), also called heterogeneous graphs, are composed of multiple types of nodes and edges, and contain comprehensive information and rich semantics. Graph neural networks (GNNs), as powerful tools for graph data, have shown superior performance on network analysis. Recently, many excellent models have been proposed to process hetero-graph data using GNNs and have achieved great success. These GNN-based heterogeneous models can be interpreted as smooth node attributes guided by graph structure, which requires all nodes to have attributes. However, this is not easy to satisfy, as some types of nodes often have no attributes in heterogeneous graphs. Previous studies take some handcrafted methods to solve this problem, which separate the attribute completion from the graph learning process and, in turn, result in poor performance. In this paper, we hold that missing attributes can be acquired by a learnable manner, and propose a general framework for Heterogeneous Graph Neural Network via Attribute Completion (HGNN-AC), including pre-learning of topological embedding and attribute completion with attention mechanism. HGNN-AC first uses existing HIN-Embedding methods to obtain node topological embedding. Then it uses the topological relationship between nodes as guidance to complete attributes for no-attribute nodes by weighted aggregation of the attributes from these attributed nodes. Our complement mechanism can be easily combined with an arbitrary GNN-based heterogeneous model making the whole system end-to-end. We conduct extensive experiments on three real-world heterogeneous graphs. The results demonstrate the superiority of the proposed framework over state-of-the-art baselines.