Dynamic graph learning aims to capture evolving structural and semantic patterns in real-world systems, such as fraud detection and recommender systems. Due to the scarcity of labeled data in real-world dynamic graphs, recent studies have introduced generative or contrastive paradigms (e.g., masked graph autoencoders or graph contrastive learning) to generate task-agnostic graph embeddings. However, these methods typically rely on complex edge-level reconstruction objectives and tailored graph augmentation strategies. This incurs substantial computational overhead when scaling to large-scale dynamic graphs. In this paper, we propose SG-JEPA, a joint spiking embedding predictive architecture for large-scale dynamic graphs. In contrast to existing self-supervised methods, SG-JEPA partitions nodes into context and target sets along the temporal dimension to learn embeddings that are predictive of each other via additional spatial-temporal information. Furthermore, through encoding sequential inputs into coarse-to-fine spike count embeddings, spiking neurons enable SG-JEPA to adapt to the varying computational constraints of downstream tasks. Extensive experiments demonstrate that SG-JEPA achieves competitive or even superior performance over discriminative baselines on node classification, while effectively scaling to the dynamic graph with 13 million edges. SG-JEPA 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
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