发表机构
Sun Yat-sen University（中山大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现实世界动态图标记数据稀缺问题，提出SG-JEPA架构，通过沿时间维度划分节点并利用脉冲神经元编码，学习相互预测的嵌入，在节点分类任务中性能优于判别基线，且训练效率和内存可扩展性高。
AI 中文摘要
动态图学习旨在捕捉现实世界系统中不断演变的结构和语义模式，如欺诈检测和推荐系统。由于现实世界动态图中标记数据稀缺，近期研究引入生成或对比范式生成与任务无关的图嵌入，但这些方法存在计算开销大的问题。本文提出用于大规模动态图的联合脉冲嵌入预测架构SG-JEPA，它沿时间维度将节点分为上下文和目标集，通过额外时空信息学习相互预测的嵌入。此外，脉冲神经元将序列输入编码为从粗到细的脉冲计数嵌入，使SG-JEPA能适应下游任务的不同计算约束。实验表明，SG-JEPA在节点分类上比判别基线有竞争力甚至更优，能有效扩展到含1300万条边的动态图，训练效率和内存可扩展性优于先前自监督动态图基线。
英文摘要
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.
