Peilan He — accepted AI-conference papers

2025

MaskDGNN: Self-Supervised Dynamic Graph Neural Networks with Activeness-aware Temporal Masking

Yiming He, Xiang Li, Zhongying Zhao, Haobing Liu, Peilan He, Yanwei Yu

IJCAI 2025

Integrating dynamics into graph neural networks (GNNs) provides deeper insights into the evolution of dynamic graphs, thereby enhancing the temporal representation in real-world dynamic network problems. Existing methods extracting critical information from dynamic graphs face two key challenges, ei

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2021

Predicting Traffic Congestion Evolution: A Deep Meta Learning Approach

Yidan Sun, Guiyuan Jiang, Siew Kei Lam, Peilan He

IJCAI 2021poster

Many efforts are devoted to predicting congestion evolution using propagation patterns that are mined from historical traffic data. However, the prediction quality is limited to the intrinsic properties that are present in the mined patterns. In addition, these mined patterns frequently fail to suf…

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