5 papers · 1 filter
From Diffusion to Reaction-Diffusion: A Dynamical-Systems View of Oversmoothing in Hypergraph Neural Networks
Zhiheng Zhou, Mengyao Zhou, Yancheng Chen +3
Higher-order couplings enhance the expressive power of hypergraph neural networks (HGNNs), but they also intensify representation collapse in deep propagation due to strong multi-w…
Latent Block-Diffusion Temporal Point Processes: A Semi-Autoregressive Framework for Asynchronous Event Sequence Generation
Shuai Zhang, Yancheng Chen, Chuan Zhou +5
Modeling and sampling from the underlying distribution of asynchronous event sequences are crucial in various real-world applications, including social networks, medical diagnosis,…
Message Tuning Outshines Graph Prompt Tuning: A Prismatic Space Perspective
Yancheng Chen, Dun Ma, Shuai Zhang +6
Graph Foundation Models (GFMs), built upon the Pre-training and Adaptation paradigm, have emerged as a research hotspot in graph learning. For GNN-based GFMs, graph prompt tuning h…
AbstainGNN: Teaching Graph Neural Networks to Abstain for Graph Classification
Xixun Lin, Zhiheng Zhou, Zhengyin Zhang +9
Graph classification is a core task in graph data mining with widespread real-world applications. Recent advances in graph neural networks (GNNs) have led to substantial performanc…
Wide & Deep Learning for Node Classification
Yancheng Chen, Wenguo Yang, Zhipeng Jiang
Wide & Deep, a simple yet effective learning architecture for recommendation systems developed by Google, has had a significant impact in both academia and industry due to its comb…