Anomaly Detection and Data Analytics for Mobile Payment Activities via Scalable Graph Representation Learning

Temporal Graph Representation Learning for Detecting Anomalies in E-payment Systems

In this paper, we consider the problem of detecting anomalies in real-world E-payment systems. The E-payment systems can be viewed as a temporal interaction graph, with nodes representing users and edges representing multidimensional transaction sequences between them. To identify illicit users in the E-payment network, we formulate it as a node classification task and apply recent advanced temporal graph representation learning approaches to solve the problem. To fully capture and model the dynamics of the E-payment network, we propose the model, Graph Temporal Edge Aggregation (GTEA), a framework of representation learning for temporal interaction graphs. Instead of aggregating all related interactions of a node over time, GTEA learns to model temporal dynamics for each edge in the graph. Such characteristics enable it to capture pairwise interactional patterns, which can be discriminative for modeling relationships. In addition, a sparsity-inducing self-attention mechanism is incorporated into the framework, which highlights important neighbors and simultaneously filters out a large number of long-tail noises. By combining interactive temporal dynamics with multi-dimensional relational dependencies in a network, GTEA can learn fine-grained representations for entities in an E-payment network, which can be used to distinguish different roles of users for anomaly detections. We demonstrate the effectiveness of GTEA over existing state-of-the-art models with three real-world E-payment datasets on node classification tasks.


  • Yiming Li, Da Sun Handason Tam, Siyue Xie, Xiaxin Liu, Qiu Fang Ying, Wing Cheong Lau, Dah Ming Chiu, Shou Zhi Chen, "Temporal Graph Representation Learning for Detecting Anomalies in E-payment Systems", 2021 International Conference on Data Mining Workshops (ICDMW), New Zealand, Dec 2021

  • Identifying Illicit Accounts in Large Scale E-payment Networks - A Graph Representation Learning Approach

    Rapid and massive adoption of mobile/ online payment services have brought new challenges to the service providers as well as regulators in safeguarding the proper uses such services/ systems. In this paper, we leverage recent advances in deep-neuralnetwork-based graph representation learning to detect abnormal/ suspicious financial transactions in real-world e-payment networks. In particular, we propose an end-to-end Graph Convolution Network (GCN)-based algorithm to learn the embeddings of the nodes and edges of a large-scale time-evolving graph. In the context of e-payment transaction graphs, the resultant node and edge embeddings can effectively characterize the user-background as well as the financial transaction patterns of individual account holders. As such, we can use the graph embedding results to drive downstream graph mining tasks such as node-classification to identify illicit accounts within the payment networks. Our algorithm outperforms state-of-the-art schemes including GraphSAGE, Gradient Boosting Decision Tree and Random Forest to deliver considerably higher accuracy (94.62% and 86.98% respectively) in classifying user accounts within 2 practical epayment transaction datasets. It also achieves outstanding accuracy (97.43%) for another biomedical entity identification task while using only edgerelated information.

    (Source code available in GitHub)


  • Da Sun Handason Tam*, Wing Cheong Lau, Bin Hu, Qiu Fang Ying, Dah Ming Chiu, Hong Liu, "Identifying Illicit Accounts in Large Scale E-payment Networks - A Graph Representation Learning Approach", IJCAI The 1st Workshop on Artificial Intelligence for Business Security, Macau, Aug 2019.

  • GraphAdaMix: Enhancing Node Representations with Graph Adaptive Mixtures

    Graph Neural Networks (GNNs) are the current state-of-the-art models in learning node representations for many predictive tasks on graphs. Typically, GNNs reuses the same set of model parameters across all nodes in the graph to improve the training efficiency and exploit the translationally-invariant properties in many datasets. However, the parameter sharing scheme prevents GNNs from distinguishing two nodes having the same local structure and that the translation invariance property may not exhibit in real-world graphs. In this paper, we present Graph Adaptive Mixtures (GraphAdaMix), a novel approach for learning node representations in a graph by introducing multiple independent GNN models and a trainable mixture distribution for each node. GraphAdaMix can adapt to tasks with different settings. Specifically, for semi-supervised tasks, we optimize GraphAdaMix using the Expectation-Maximization (EM) algorithm, while in unsupervised settings, GraphAdaMix is trained following the paradigm of contrastive learning. We evaluate GraphAdaMix on ten benchmark datasets with extensive experiments. GraphAdaMix is demonstrated to consistently boost state-of-the-art GNN variants in semi-supervised and unsupervised node classification tasks. The code of GraphAdaMix is available online.


  • Da Sun Handason Tam, Siyue Xie, Wing Cheong Lau, “GraphAdaMix: Enhancing Node Representations with Graph Adaptive Mixtures” International Conference on Artificial Intelligence and Statistics, May. 2022.

  • CoCoS: Enhancing Semi-supervised Learning on Graphs with Unlabeled Data via Contrastive Context Sharing

    Graph Neural Networks (GNNs) have recently become a popular framework for semi-supervised learning on graph-structured data. However, typical GNN models heavily rely on labeled data in the learning process, while ignoring or paying little attention to the data that are unlabeled but available. To make full use of available data, we propose a generic framework, Contrastive Context Sharing (CoCoS), to enhance the learning capacity of GNNs for semi-supervised tasks. By sharing the contextual information among nodes estimated to be in the same class, different nodes can be correlated even if they are unlabeled and remote from each other in the graph. Models can therefore learn different combinations of contextual patterns, which improves the robustness of node representations. Additionally, motivated by recent advances in self-supervised learning, we augment the context sharing strategy by integrating with contrastive learning, which naturally correlates intra-class and inter-class data. Such operations utilize all available data for training and effectively improve a model's learning capacity. CoCoS can be easily extended to a wide range of GNN-based models with little computational overheads. Extensive experiments show that CoCoS considerably enhances typical GNN models, especially when labeled data are sparse in a graph, and achieves state-of-the-art or competitive results in real-world public datasets. The code of CoCoS is available online.


  • Siyue Xie, Da Sun Handason, Wing Cheong Lau, “CoCoS: Enhancing Semi-supervised Learning on Graphs with Unlabeled Data via Contrastive Context Sharing” Proceedings of the AAAI Conference on Artificial Intelligence, June. 2022.

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