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, GNNs with a fixed message passing mechanism, e.g., GCN, GAT, and GraphSAGE, exhibit inconsistent performance across different graphs. To address this issue, we propose Mixture of Message Passing (MoMP). Code is made publicly available at https://github.com/Tinmomo/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''. Specifically, MoMP employs a gating network to perform edge-level routing, adaptively selecting the most suitable expert to process the message for each edge. To ensure training stability and efficacy, we also introduce a partially-shared expert architecture, reframing the learning task for sparse experts into a residual from a globally-learned general mechanism. Extensive experiments on several homophilic graph benchmarks demonstrate that the proposed method not only outperforms strong baselines but also provides significant interpretability. To the best of our knowledge, our work is the first to fuse the MoE paradigm directly with the GNN message passing mechanism, paving the way for developing adaptive GNNs.
Zhaojun Luo, Jintang Li, Yuchang Zhu et al.· Proceedings of the 32nd ACM...· 0 citations
Graph neural networks (GNNs) frequently encounter group fairness issues, often yielding biased predictions against specific demographic groups defined by sensitive attributes such as gender or race. While this challenge has motivated extensive research, most existing solutions rely on the strong assumption that demographics are fully available. To bypass this strict requirement, a few recent studies have attempted to use predicted demographics as proxies to enforce fairness constraints. However, predicted demographics may be inaccurate, resulting in the failure to improve fairness. In this work, we investigate the problem of graph fairness without demographic information and avoid the utilization of predicted demographics. Motivated by our observation that the gradient distributions of misclassified nodes implicitly encode demographic information, we first propose GradDist, a gradient-based metric that quantifies bias by measuring the distance between local modes within these distributions. To mitigate this bias, we propose Gradient-to-Fairness (Grad2Fair), a gradient-guided approach for group fairness without demographics. Due to the potential demographics in gradients, Grad2Fair directly leverages gradients to debias and eliminates demographic prediction, thereby enabling stable fairness performance. Experiments on several real-world datasets demonstrate the effectiveness of Grad2Fair, as evidenced by superior performance over baselines in most cases. Our code is available at https://github.com/ZzoomD/Grad2Fair.
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 graph baselines.
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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