Online Continual Graph Learning

• URL: http://arxiv.org/abs/2508.03283v1
• Date: Tue, 05 Aug 2025 10:05:09 GMT
• Title: Online Continual Graph Learning
• Authors: Giovanni Donghi, Luca Pasa, Daniele Zambon, Cesare Alippi, Nicolò Navarin,
• Abstract summary: The aim of Continual Learning (CL) is to learn new tasks incrementally while avoiding catastrophic forgetting.<n>Online Continual Learning (OCL) specifically focuses on learning efficiently from a continuous stream of data with shifting distribution.
• Score: 22.132012209450004
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The aim of Continual Learning (CL) is to learn new tasks incrementally while avoiding catastrophic forgetting. Online Continual Learning (OCL) specifically focuses on learning efficiently from a continuous stream of data with shifting distribution. While recent studies explore Continual Learning on graphs exploiting Graph Neural Networks (GNNs), only few of them focus on a streaming setting. Yet, many real-world graphs evolve over time, often requiring timely and online predictions. Current approaches, however, are not well aligned with the standard OCL setting, partly due to the lack of a clear definition of online Continual Learning on graphs. In this work, we propose a general formulation for online Continual Learning on graphs, emphasizing the efficiency requirements on batch processing over the graph topology, and providing a well-defined setting for systematic model evaluation. Finally, we introduce a set of benchmarks and report the performance of several methods in the CL literature, adapted to our setting.

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