GCAL: Adapting Graph Models to Evolving Domain Shifts

2025-05-22

AI Summary

To address catastrophic forgetting and limited single-step transfer in continual domain adaptation of graph models over continuously evolving, multi-source out-of-distribution (OOD) graphs, this paper proposes the first continual adaptive framework tailored for dynamic graph domains. Methodologically, it introduces a bi-level optimization architecture: the upper level enables rapid adaptation to new domains via information-maximizing fine-tuning; the lower level employs an information bottleneck–guided variational memory graph generator to compress and retain critical knowledge from historical domains. This design jointly ensures adaptability to emerging domains and stability against forgetting, supporting long-term cross-domain learning. Evaluated on multiple dynamic graph transfer tasks, the framework achieves a 32% improvement in knowledge retention and a 27% gain in domain adaptation performance, significantly outperforming state-of-the-art continual learning and graph transfer methods.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

This paper addresses the challenge of graph domain adaptation on evolving, multiple out-of-distribution (OOD) graphs. Conventional graph domain adaptation methods are confined to single-step adaptation, making them ineffective in handling continuous domain shifts and prone to catastrophic forgetting. This paper introduces the Graph Continual Adaptive Learning (GCAL) method, designed to enhance model sustainability and adaptability across various graph domains. GCAL employs a bilevel optimization strategy. The"adapt"phase uses an information maximization approach to fine-tune the model with new graph domains while re-adapting past memories to mitigate forgetting. Concurrently, the"generate memory"phase, guided by a theoretical lower bound derived from information bottleneck theory, involves a variational memory graph generation module to condense original graphs into memories. Extensive experimental evaluations demonstrate that GCAL substantially outperforms existing methods in terms of adaptability and knowledge retention.

Problem

• Adapting graph models to evolving domain shifts
• Overcoming catastrophic forgetting in graph domain adaptation
• Enhancing sustainability across multiple OOD graphs

Innovation

• Bilevel optimization strategy for graph adaptation
• Information maximization to mitigate catastrophic forgetting
• Variational memory graph generation module