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arXiv Machine Learning By Tianming Zhang, Jinxu Wang, Yixuan Zhang, Zhengyi Yang, Chenyu Hou, Bin Cao, Jing Fan

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

Read the original on arXiv Machine Learning →

DISRL is a Dual-Scale Incremental Semi-Supervised Reinforcement Learning framework designed for dynamic local community detection. It tackles temporal supervision uncertainty by generating seed-specific guidance from sparse known communities and mitigates unbounded error cascades through a dual-scale community exploration module that separates short-term adaptation from long-term parameter consolidation. Experiments on a newly constructed dynamic graph benchmark show DISRL consistently outperforms state‑of‑the‑art baselines across various dynamic settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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