标题： GraphKeeper：通过知识解缠和保存进行图领域增量学习 显示英文标题

标题： GraphKeeper: Graph Domain-Incremental Learning via Knowledge Disentanglement and Preservation

摘要： 图增量学习（GIL），通过顺序知识获取持续更新图模型，最近引起了广泛关注。 然而，现有的GIL方法主要关注单一领域内的任务增量和类别增量场景。 图域增量学习（Domain-IL），旨在跨多个图域更新模型，在图基础模型（GFMs）的发展下变得至关重要，但在文献中仍鲜有研究。 在本文中，我们提出了通过知识纠缠与保留进行图域增量学习（GraphKeeper），从嵌入偏移和决策边界偏差的角度解决Domain-IL场景中的灾难性遗忘问题。 具体而言，为了防止增量图域之间的嵌入偏移和混淆，我们首先提出了领域特定的参数高效微调，以及域内和域间解缠目标。 随后，为了保持稳定的决策边界，我们引入无偏差的知识保留以持续适应增量域。 此外，对于具有不可观测域的图，我们进行领域感知的分布鉴别以获得精确的嵌入。 大量实验表明，所提出的GraphKeeper在运行者之后取得了最先进的结果，提升了6.5%~16.6%，且遗忘可忽略不计。 此外，我们展示了GraphKeeper可以无缝集成到各种代表性的GFMs中，突显了其广泛的应用潜力。 显示英文摘要

摘要： Graph incremental learning (GIL), which continuously updates graph models by sequential knowledge acquisition, has garnered significant interest recently. However, existing GIL approaches focus on task-incremental and class-incremental scenarios within a single domain. Graph domain-incremental learning (Domain-IL), aiming at updating models across multiple graph domains, has become critical with the development of graph foundation models (GFMs), but remains unexplored in the literature. In this paper, we propose Graph Domain-Incremental Learning via Knowledge Dientanglement and Preservation (GraphKeeper), to address catastrophic forgetting in Domain-IL scenario from the perspectives of embedding shifts and decision boundary deviations. Specifically, to prevent embedding shifts and confusion across incremental graph domains, we first propose the domain-specific parameter-efficient fine-tuning together with intra- and inter-domain disentanglement objectives. Consequently, to maintain a stable decision boundary, we introduce deviation-free knowledge preservation to continuously fit incremental domains. Additionally, for graphs with unobservable domains, we perform domain-aware distribution discrimination to obtain precise embeddings. Extensive experiments demonstrate the proposed GraphKeeper achieves state-of-the-art results with 6.5%~16.6% improvement over the runner-up with negligible forgetting. Moreover, we show GraphKeeper can be seamlessly integrated with various representative GFMs, highlighting its broad applicative potential.