aiqbench analysis
This arXiv preprint proposes a semi-supervised reinforcement learning method for tracking local communities around seed nodes as a graph changes over time. It targets two problems: sparse, outdated labels under topology shifts, and error accumulation when a policy is transferred across many snapshots. The reported gains are based on the authors' own dynamic-graph benchmark and baselines, so readers should wait for independent replication before treating the results as established.
DISRL: Dual-Scale Incremental Semi-Supervised Reinforcement Learning for Dynamic Community Tracking
arXiv:2610.29315v1 Announce Type: new 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 However, it faces two critical challenges: temporal supervision uncertainty, where sparse and outdated labels fail to provide seed-specific guidance under topology changes, and unbounded error cascades, where sequential policy transfer accumulates exploration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.
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 However, it faces two critical challenges: temporal supervision uncertainty, where sparse and outdated labels fail to provide seed-specific guidance under topology changes, and unbounded error cascades, where sequential policy transfer accumulates exploration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.
Machine Learning (cs.LG)
arXiv:2610.29315 [cs.LG]
(or arXiv:2610.29315v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.29315 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 222 words