动态文本属性图上的边分类迁移学习
Transfer Learning for Edge Classification on Dynamic Text-Attributed Graphs
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中文总结 AI 辅助
针对动态文本属性图边分类的分布偏移问题，提出LODO协议和STSA方法，通过时空编码与对比语义预测，优于现有方法。
中文摘要 AI 辅助
学习动态文本属性图（DyTAGs）的可迁移表示要求模型能够捕获跨领域持续存在的底层交互动态。然而，现有方法往往过度拟合特定领域的结构、时间和语义模式，导致在分布偏移下边分类性能受限。为揭示并解决这一问题，我们正式建立了用于DyTAGs边分类的留一域外（LODO）迁移学习协议。在该协议下，我们证明了最先进的动态图学习自监督方法在迁移到未见领域时表现不佳。令人惊讶的是，现有方法不如我们引入的一种结构和时间无关的事件袋（BoE）模型，该模型仅输入节点和边文本特征的无序序列。基于BoE，我们提出了时空语义对齐（STSA），它集成了一种时空编码器，将时间增量和节点出现频率的表示融合到一个统一流形中。STSA通过对比语义预测目标进行训练，该目标将边表示锚定到一个由预训练语言模型初始化的多领域文本潜在空间，提供了一个稳健的先验，其性能优于BoE及我们评估的所有现有方法。
英文摘要
Learning transferable representations for dynamic text-attributed graphs (DyTAGs) requires models to capture underlying interaction dynamics that persist across domains. However, existing methods tend to overfit to domain-specific structural, temporal, and semantic patterns, limiting edge classification performance under distribution shifts. To expose and address this, we formally establish a leave-one-domain-out (LODO) transfer learning protocol for edge classification on DyTAGs. Under this protocol, we demonstrate that state-of-the-art self-supervised methods for dynamic graph learning perform poorly when transferred to unseen domains. Strikingly, existing methods underperform a structurally and temporally unaware Bag of Events (BoE) model we introduce, which inputs only unordered sequences of node and edge text features. Proceeding from the BoE, we propose Spatio-Temporal Semantic Alignment (STSA), which integrates a spatio-temporal encoder that fuses representations of time deltas and node occurrence frequencies into a unified manifold. STSA is trained with a Contrastive Semantic Forecasting objective, which anchors edge representations to a multi-domain textual latent space initialized by a pretrained language model, providing a robust prior that outperforms BoE and all existing methods we evaluate.
