{"ID":2845843,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.04712","arxiv_id":"2511.04712","title":"NCSAC: Effective Neural Community Search via Attribute-augmented Conductance","abstract":"Identifying locally dense communities closely connected to the user-initiated query node is crucial for a wide range of applications. Existing approaches either solely depend on rule-based constraints or exclusively utilize deep learning technologies to identify target communities. Therefore, an important question is proposed: can deep learning be integrated with rule-based constraints to elevate the quality of community search? In this paper, we affirmatively address this question by introducing a novel approach called Neural Community Search via Attribute-augmented Conductance, abbreviated as NCSAC. Specifically, NCSAC first proposes a novel concept of attribute-augmented conductance, which harmoniously blends the (internal and external) structural proximity and the attribute similarity. Then, NCSAC extracts a coarse candidate community of satisfactory quality using the proposed attribute-augmented conductance. Subsequently, NCSAC frames the community search as a graph optimization task, refining the candidate community through sophisticated reinforcement learning techniques, thereby producing high-quality results. Extensive experiments on six real-world graphs and ten competitors demonstrate the superiority of our solutions in terms of accuracy, efficiency, and scalability. Notably, the proposed solution outperforms state-of-the-art methods, achieving an impressive F1-score improvement ranging from 5.3\\% to 42.4\\%. For reproducibility purposes, the source code is available at https://github.com/longlonglin/ncsac.","short_abstract":"Identifying locally dense communities closely connected to the user-initiated query node is crucial for a wide range of applications. Existing approaches either solely depend on rule-based constraints or exclusively utilize deep learning technologies to identify target communities. Therefore, an important question is p...","url_abs":"https://arxiv.org/abs/2511.04712","url_pdf":"https://arxiv.org/pdf/2511.04712v1","authors":"[\"Longlong Lin\",\"Quanao Li\",\"Miao Qiao\",\"Zeli Wang\",\"Jin Zhao\",\"Rong-Hua Li\",\"Xin Luo\",\"Tao Jia\"]","published":"2025-11-05T15:28:44Z","proceeding":"cs.SI","tasks":"[\"cs.SI\"]","methods":"[\"Reinforcement Learning\"]","has_code":false,"code_links":[{"ID":607389,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2845843,"paper_url":"https://arxiv.org/abs/2511.04712","paper_title":"NCSAC: Effective Neural Community Search via Attribute-augmented Conductance","repo_url":"https://github.com/longlonglin/ncsac","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}