DRIFT: A Benchmark for Task-Free Continual Graph Learning with Continuous Distribution Shifts

• URL: http://arxiv.org/abs/2605.12998v2
• Date: Thu, 14 May 2026 07:50:34 GMT
• Title: DRIFT: A Benchmark for Task-Free Continual Graph Learning with Continuous Distribution Shifts
• Abstract summary: Continual graph learning (CGL) aims to learn from dynamically evolving graphs while mitigating catastrophic forgetting.<n>Existing approaches typically adopt a task-based formulation, where the data stream is partitioned into a sequence of discrete tasks with pre-defined boundaries.<n>We revisit continual graph learning from a task-free perspective to better reflect realistic non-stationary environments.
• Score: 18.827073961587136
• License: http://creativecommons.org/licenses/by/4.0/
• 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 \emph{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 https://github.com/UConn-DSIS/DRIFT.

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