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Poster
CGLB: Benchmark Tasks for Continual Graph Learning
Xikun Zhang · Dongjin Song · Dacheng Tao

Thu Dec 01 02:00 PM -- 04:00 PM (PST) @ Hall J #1033
in Poster Session 6 »
Continual learning on graph data, which aims to accommodate new tasks over newly emerged graph data while maintaining the model performance over existing tasks, is attracting increasing attention from the community. Unlike continual learning on Euclidean data ($\textit{e.g.}$, images, texts, etc.) that has established benchmarks and unified experimental settings, benchmark tasks are rare for Continual Graph Learning (CGL). Moreover, due to the variety of graph data and its complex topological structures, existing works adopt different protocols to configure datasets and experimental settings. This creates a great obstacle to compare different techniques and thus hinders the development of CGL. To this end, we systematically study the task configurations in different application scenarios and develop a comprehensive Continual Graph Learning Benchmark (CGLB) curated from different public datasets. Specifically, CGLB contains both node-level and graph-level continual graph learning tasks under task-incremental (currently widely adopted) and class-incremental (more practical, challenging, yet underexplored) settings, as well as a toolkit for training, evaluating, and visualizing different CGL methods. Within CGLB, we also systematically explain the difference among these task configurations by comparing them to classical continual learning settings. Finally, we comprehensively compare state-of-the-art baselines on CGLB to investigate their effectiveness. Given CGLB and the developed toolkit, the barrier to exploring CGL has been greatly lowered and researchers can focus more on the model development without worrying about tedious work on pre-processing of datasets or encountering unseen pitfalls. The benchmark and the toolkit are available through https://github.com/QueuQ/CGLB.
Keywords: continual learning · Graph Representation Learning

Author Information

Xikun Zhang (the University of Sydney)
Dongjin Song (University of Connecticut)
Dacheng Tao (University of Technology, Sydney)

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