Pak Lon Ip — accepted AI-conference papers

2026

Connectivity-Guided Sparsification of 2-FWL GNNs: Preserving Full Expressivity with Improved Efficiency

Rongqin Chen, Fan Mo, Pak Lon Ip, Shenghui Zhang, Dan Wu, Ye Li +1

AAAI 2026technical

Higher-order Graph Neural Networks (HOGNNs) based on the 2-FWL test achieve superior expressivity by modeling 2-node and 3-node interactions, but incur cubic computational cost. Existing efficiency methods typically reduce this burden at the expense of expressivity. We propose Co-Sparsify, a connect

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2025

Tokenphormer: Structure-aware Multi-token Graph Transformer for Node Classification

Zijie Zhou, Zhaoqi Lu, Xuekai Wei, Rongqin Chen, Shenghui Zhang, Pak Lon Ip +1

AAAI 2025technical

Graph Neural Networks (GNNs) are widely used in graph data mining tasks. Traditional GNNs follow a message passing scheme that can effectively utilize local and structural information. However, the phenomena of over-smoothing and over-squashing limit the receptive field in message passing processes.…

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