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arXiv · 2610.29315

DISRL: Dual-Scale Incremental Semi-Supervised Reinforcement Learning for Dynamic Community Tracking

Tianming Zhang·Jinxu Wang·Yixuan Zhang·Zhengyi Yang·Chenyu Hou·Bin Cao·Jing Fan
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Abstract

Dynamic local community detection tracks the evolving community of designated seed nodes across graph snapshots. However, it faces two critical challenges: temporal supervision uncertainty, where sparse and outdated labels fail to provide seed-specific guidance under topology changes, and unbounded error cascades, where sequential policy transfer accumulates exploration errors over long horizons. To address these challenges, we propose DISRL, a Dual-scale Incremental Semi-supervised Reinforcement Learning framework. DISRL introduces a supervision construction module that dynamically generates seed-specific guidance from sparse known communities, and a dual-scale community exploration module that stabilizes policy evolution by decoupling short-term adaptation within temporal windows from long-term parameter consolidation across windows. Furthermore, we construct a dynamic graph benchmark with diverse community evolution scenarios. Extensive experiments demonstrate that DISRL consistently outperforms state-of-the-art baselines under various dynamic settings.

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Tianming Zhang, Jinxu Wang, Yixuan Zhang, Zhengyi Yang, Chenyu Hou, Bin Cao, Jing Fan. 2026-10-21. DISRL: Dual-Scale Incremental Semi-Supervised Reinforcement Learning for Dynamic Community Tracking. https://arxiv.org/abs/2610.29315

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