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Changlong He

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3 papers

2 author rows

ECAI Conference 2025 Conference Paper

Lightning Decoupled Graph Neural Architecture Search for Fraud Detection

Tairan Huang
Changlong He
Yili Wang 0005
Jianliang Gao

Graph neural networks (GNNs) for fraud detection has received extensive attention, where malicious behaviors often exhibit complex relational patterns. Despite their success, the GNN architecture design of existing graph-based fraud detection methods requires significant manual work and expert knowledge. The application of manually designed architectures to diverse real-world scenarios remains a huge time cost, as it requires numerous parameter tuning for varying conditions. Moreover, the GNN-based methods suffer from the over-smoothing problem during multi-layer message passing, which limits the performance in the fraud detection task. To address these problems, we propose the Automatic lightning decoupled Graph neural architecture search for Fraud Detection (AutoGFD). Specifically, AutoGFD designs the decoupled search algorithm to automatically construct the optimal architecture from the specialized architecture search space for the fraud detection task, which can effectively solve the over-smoothing problem. In addition, AutoGFD designs the lightning search tuning mechanism to improve the efficiency of architecture estimation. As far as we know, AutoGFD is the first attempt to design decoupled architecture search for fraud detection, which can automatically search for optimal architectures in different fraud detection scenarios without manual design and expert knowledge. The experimental results based on multiple benchmark datasets show that AutoGFD can achieve significant performance advantages over state-of-the-art baseline methods.

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NeurIPS Conference 2025 Conference Paper

Simple and Efficient Heterogeneous Temporal Graph Neural Network

Yili Wang
Tairan Huang
Changlong He
Qiutong Li
Jianliang Gao

Heterogeneous temporal graphs (HTGs) are ubiquitous data structures in the real world. Recently, to enhance representation learning on HTGs, numerous attention-based neural networks have been proposed. Despite these successes, existing methods rely on a decoupled temporal and spatial learning paradigm, which weakens interactions of spatio-temporal information and leads to a high model complexity. To bridge this gap, we propose a novel learning paradigm for HTGs called Simple and Efficient Heterogeneous Temporal Graph Neural Network (SE-HTGNN). Specifically, we innovatively integrate temporal modeling into spatial learning via a novel dynamic attention mechanism, which substantially reduces model complexity while enhancing discriminative representation learning on HTGs. Additionally, to comprehensively and adaptively understand HTGs, we leverage large language models to prompt SE-HTGNN, enabling the model to capture the implicit properties of node types as prior knowledge. Extensive experiments demonstrate that SE-HTGNN achieves up to 10× speed-up over the state-of-the-art and latest baseline while maintaining the best forecasting accuracy.

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ECAI Conference 2024 Conference Paper

Relation Time-Aware Heterogeneous Dynamic Graph Neural Networks

Yili Wang 0005
Jiamin Chen
Qiutong Li
Changlong He
Jianliang Gao

Heterogeneous dynamic graph neural networks (HDGNNs) are effective methods for processing heterogeneous temporal graphs (HTGs), which serve as ubiquitous data structures in real-world scenarios. The previous HDGNN paradigm obtains representations of future target nodes by mining the spatial heterogeneity and temporal dependence of node attributes, ignoring the learning of relation temporal dependence. However, through experience, we find that the learning of relation temporal dependence, which describes the evolving trends in the importance of neighbors under a certain relation, is beneficial for representation learning of HTGs. To bridge this gap, we propose a novel end-to-end heterogeneous temporal graph learning paradigm called Relation Time-aware Heterogeneous Dynamic Graph Neural Networks (ReTag). Compared to previous HDGNNs, ReTag extracts the temporal dependence of relations from historical relation information and the evolving node attributes to drive subsequent spatio-temporal representation learning. As far as we know, ReTag is the first attempt to perform learning of the temporal dependence of relation, which can generate a more effective representation for different downstream tasks of HTGs. The experimental results of different downstream tasks of HTGs based on multiple benchmark datasets show that ReTag can obtain obvious performance advantages compared with the sota baseline method.

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