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Most existing studies in the literature only propose\n            <jats:italic toggle=\"yes\">a single and static model<\/jats:italic>\n            based on\n            <jats:italic toggle=\"yes\">a particular subgraph<\/jats:italic>\n            (e.g.,\n            <jats:italic toggle=\"yes\">k<\/jats:italic>\n            -core,\n            <jats:italic toggle=\"yes\">k<\/jats:italic>\n            -truss, quasi-clique, and learning-based component). These fixed models are hard to find exact community answers on all datasets and fit with different underlying desires of users and queries. This implies that the\n            <jats:italic toggle=\"yes\">community search<\/jats:italic>\n            task needs human-in-loop\n            <jats:italic toggle=\"yes\">interactions<\/jats:italic>\n            , which allows users to give feedback and dynamically advise community refinement.\n          <\/jats:p>\n          <jats:p>\n            To tackle the above issues, we formulate and study the problem of\n            <jats:italic toggle=\"yes\">interactive community search<\/jats:italic>\n            , which allows users to add\/delete vertices for improving community answers in a few rounds of interactions. We first summarize dozens of existing community models and develop an integrated notation system M(\n            <jats:italic toggle=\"yes\">G, M, O, P<\/jats:italic>\n            ) to describe them all. Then, we propose a flexible approach to interactive community search over graphs called GICS-framework. The successful principle of GICS-framework lies on three key components:\n            <jats:italic toggle=\"yes\">personalized adding\/deleting recommendation, parameter auto-tuning<\/jats:italic>\n            , and\n            <jats:italic toggle=\"yes\">fast partial refinement.<\/jats:italic>\n            We develop efficient algorithms and successfully deploy three community models on our GICS-framework. We further analyze algorithm complexity of GICS-framework by illustrating one instance model in detail. Extensive experiments on ground-truth communities demonstrate that our interaction of GICS-framework improves F1-score accuracy by 22% against state-of-the-art competitors, and gives users real-time responses within one second.\n          <\/jats:p>","DOI":"10.14778\/3725688.3725720","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T14:19:21Z","timestamp":1756477161000},"page":"1977-1990","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["A Flexible Framework for Query-Oriented Interactive Community Search"],"prefix":"10.14778","volume":"18","author":[{"given":"Longxu","family":"Sun","sequence":"first","affiliation":[{"name":"Hong Kong Baptist University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Huang","sequence":"additional","affiliation":[{"name":"Hong Kong Baptist University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiannan","family":"Wang","sequence":"additional","affiliation":[{"name":"Huawei Technologies and Simon Fraser University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianliang","family":"Xu","sequence":"additional","affiliation":[{"name":"Hong Kong Baptist University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,8,29]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.14778\/3137628.3137640"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.14778\/3407790.3407812"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-015-0422-1"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.14778\/3570690.3570700"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.14778\/3603581.3603595"},{"key":"e_1_2_1_6_1","volume-title":"Contextual community search over large social networks","author":"Chen Lu","unstructured":"Lu Chen, Chengfei Liu, Kewen Liao, Jianxin Li, and Rui Zhou. 2019. 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