"Transaction fraud" complaint verification, i.e., verifying whether a transaction corresponding to a complaint is fraudulent, is particularly critical to prevent economic loss. Compared with traditional fraud pre-transaction detection, complaint verification puts forward higher requirements: 1)an individual tends to exhibit different identities in different complaints, e.g., complainant or respondent, requiring the model to capture identity-related representations corresponding to the complaint; 2)the fraud ways evolve frequently to confront detection, requiring the model to perform stably under different fraud ways. Previous methods mainly focused on fraud pre-transaction detection, utilizing the historical information of users or conduct message passing based GNNs on relationship networks. However, they rarely consider capturing various identity-related representations and ignore the evolution of fraud ways, leading to failure in complaint verification. To address the above challenges, we propose the mutual information based dual level graph neural network, namely MIDLG, which defines a complaint as a super-node consisting of involved individuals, and characterizes the individual over node-level and super-node-level. Furthermore, the mutual information minimization objective is proposed based on "complaint verification-causal graph" to decouple the model prediction from relying on specific fraud ways, and thus achieve stability. MIDLG achieves SOTA results through extensive experiments in complaint verification on WeChat Finance, one online payment service serving more than 600 million users in China.

Clustering is a fundamental task in data analysis. Recently, deep clustering, which derives inspiration primarily from deep learning approaches, achieves state-of-the-art performance and has attracted considerable attention. Current deep clustering methods usually boost the clustering results by means of the powerful representation ability of deep learning, e.g., autoencoder, suggesting that learning an effective representation for clustering is a crucial requirement. The strength of deep clustering methods is to extract the useful representations from the data itself, rather than the structure of data, which receives scarce attention in representation learning. Motivated by the great success of Graph Convolutional Network (GCN) in encoding the graph structure, we propose a Structural Deep Clustering Network (SDCN) to integrate the structural information into deep clustering. Specifically, we design a delivery operator to transfer the representations learned by autoencoder to the corresponding GCN layer, and a dual self-supervised mechanism to unify these two different deep neural architectures and guide the update of the whole model. In this way, the multiple structures of data, from low-order to high-order, are naturally combined with the multiple representations learned by autoencoder. Furthermore, we theoretically analyze the delivery operator, i.e., with the delivery operator, GCN improves the autoencoder-specific representation as a high-order graph regularization constraint and autoencoder helps alleviate the over-smoothing problem in GCN. Through comprehensive experiments, we demonstrate that our propose model can consistently perform better over the state-of-the-art techniques.

Multi-view graph clustering, which seeks a partition of the graph with multiple views that often provide more comprehensive yet complex information, has received considerable attention in recent years. Although some efforts have been made for multi-view graph clustering and achieve decent performances, most of them employ shallow model to deal with the complex relation within multi-view graph, which may seriously restrict the capacity for modeling multi-view graph information. In this paper, we make the first attempt to employ deep learning technique for attributed multi-view graph clustering, and propose a novel task-guided One2Multi graph autoencoder clustering framework. The One2Multi graph autoencoder is able to learn node embeddings by employing one informative graph view and content data to reconstruct multiple graph views. Hence, the shared feature representation of multiple graphs can be well captured. Furthermore, a self-training clustering objective is proposed to iteratively improve the clustering results. By integrating the self-training and autoencoder’s reconstruction into a unified framework, our model can jointly optimize the cluster label assignments and embeddings suitable for graph clustering. Experiments on real-world attributed multi-view graph datasets well validate the effectiveness of our model.