Peihan Wu — accepted AI-conference papers

2026

DyC-STG: Dynamic Causal Spatio-Temporal Graph Network for Real-time Data Credibility Analysis in IoT

Guanjie Cheng, Boyi Li, Peihan Wu, Feiyi Chen, Xinkui Zhao, Mengying Zhu +1

AAAI 2026technical

The wide spreading of Internet of Things (IoT) sensors generates vast spatio-temporal data streams, but ensuring data credibility is a critical yet unsolved challenge for applications like smart homes. While spatio-temporal graph (STG) models are a leading paradigm for such data, they often fall sho

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2025

Wave-driven Graph Neural Networks with Energy Dynamics for Over-smoothing Mitigation

Peihan Wu, Hongda Qi, Sirong Huang, Dongdong An, Jie Lian, Qin Zhao

IJCAI 2025

Over-smoothing is a persistent challenge in Graph Neural Networks (GNNs), where node embeddings become indistinguishable as network depth increases, fundamentally limiting their effectiveness on tasks requiring fine-grained distinctions. This issue arises from the reliance on diffusion-based propaga

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