The message passing mechanism, which updates node representations by exchanging messages with their neighbors, plays a critical role in graph neural networks (GNNs) for capturing structural patterns. Since the single message passing mechanism lacks the flexibility to handle graphs with differing node feature quality, G...
Zhaojun Luo, Jintang Li, Yuchang Zhu et al.· Proceedings of the 32nd ACM...· 0 citations
This work investigates the problem of graph fairness without demographic information and proposes Grad2Fair, a gradient-guided approach for group fairness without demographics that directly leverages gradients to debias and eliminates demographic prediction, thereby enabling stable fairness performance.
SG-JEPA is proposed, a joint spiking embedding predictive architecture for large-scale dynamic graphs that avoids the complex machinery (negative sampling, graph augmentations, edge-level reconstruction, etc.), resulting in superior training efficiency and memory scalability compared with prior self-supervised dynamic...
Huizhe Zhang, Yuchang Zhu, Huazhen Zhong et al.· 0 citations
This work proposes Mixture of Message Passing (MoMP), a novel mechanism that incorporates the Mixture of Experts (MoE) paradigm directly into the message passing mechanism of GNNs, treating different message passing mechanisms as ''experts''.
Zhaojun Luo, Jintang Li, Yuchang Zhu et al.· Proceedings of the 32nd ACM...· 0 citations
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