Abstract:Continual graph learning (CGL) aims to learn from dynamically evolving graphs while mitigating catastrophic forgetting. Existing CGL approaches typically adopt a task-based formulation, where the data stream is partitioned into a sequence of discrete tasks with pre-defined boundaries. However, such assumptions rarely hold in real-world environments, where data distributions evolve continuously and task identity is often unavailable. To better reflect realistic non-stationary environments, we revisit continual graph learning from a task-free perspective. We propose a unified formulation that models the data stream as a time-varying mixture of latent task distributions, enabling continuous modeling of distribution drift. Based on this formulation, we construct DRIFT, a benchmark that spans a spectrum of transition dynamics ranging from hard task switches to smooth distributional drift through a Gaussian parameterization. We evaluate representative continual learning methods under this task-free setting and observe substantial performance degradation compared to traditional task-based protocols. Our findings indicate that many existing approaches implicitly rely on task boundary information and struggle under realistic task-free graph streams. This work highlights the importance of studying continual graph learning under realistic non-stationary conditions and provides a benchmark for future research in this direction. Our code is available at this https URL.
| Comments: | 20 pages |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.12998 [cs.LG] |
| (or arXiv:2605.12998v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.12998 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Guiquan Sun [view email]
[v1]
Wed, 13 May 2026 04:54:46 UTC (1,935 KB)