{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T03:06:08Z","timestamp":1787022368387,"version":"build-2736575974"},"reference-count":24,"publisher":"Association for Computing Machinery (ACM)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2021,2]]},"abstract":"<jats:p>\n                    Searching a community containing a given query vertex in an online social network enjoys wide applications like recommendation, team organization, etc. When applied to real-life networks, the existing approaches face two major limitations. First, they usually take two steps,\n                    <jats:italic>i.e.<\/jats:italic>\n                    , crawling a large part of the network first and then finding the community next, but the entire network is usually too big and most of the data are not interesting to end users. Second, the existing methods utilize hand-crafted rules to measure community membership, while it is very difficult to define effective rules as the communities are flexible for different query vertices. In this paper, we propose an Interactive Community Search method based on Graph Neural Network (shortened by ICS-GNN) to locate the target community over a subgraph collected on the fly from an online network. Specifically, we recast the community membership problem as a vertex classification problem using GNN, which captures similarities between the graph vertices and the query vertex by combining content and structural features seamlessly and flexibly under the guide of users' labeling. We then introduce a\n                    <jats:italic>k<\/jats:italic>\n                    -sized Maximum-GNN-scores (shortened by\n                    <jats:italic>kMG<\/jats:italic>\n                    ) community to describe the target community. We next discover the target community iteratively and interactively. In each iteration, we build a candidate subgraph using the crawled pages with the guide of the query vertex and labeled vertices, infer the vertex scores with a GNN model trained on the subgraph, and discover the\n                    <jats:italic>kMG<\/jats:italic>\n                    community which will be evaluated by end users to acquire more feedback. Besides, two optimization strategies are proposed to combine ranking loss into the GNN model and search more space in the target community location. We conduct the experiments in both offline and online real-life data sets, and demonstrate that ICS-GNN can produce effective communities with low overhead in communication, computation, and user labeling.\n                  <\/jats:p>","DOI":"10.14778\/3447689.3447704","type":"journal-article","created":{"date-parts":[[2021,4,12]],"date-time":"2021-04-12T12:20:06Z","timestamp":1618230006000},"page":"1006-1018","source":"Crossref","is-referenced-by-count":73,"title":["ICS-GNN"],"prefix":"10.14778","volume":"14","author":[{"given":"Jun","family":"Gao","sequence":"first","affiliation":[{"name":"Peking University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiazun","family":"Chen","sequence":"additional","affiliation":[{"name":"Peking University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhao","family":"Li","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhejiang Lab, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,4,12]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Zhengdao Chen Lisha Li and Joan Bruna. 2019. Supervised Community Detection with Line Graph Neural Networks. In ICLR.  Zhengdao Chen Lisha Li and Joan Bruna. 2019. Supervised Community Detection with Line Graph Neural Networks. In ICLR."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2463676.2463722"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.14778\/2994509.2994538"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-019-00556-x"},{"key":"e_1_2_1_5_1","unstructured":"Matthias Fey and Jan Eric Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. http:\/\/arxiv.org\/pdf\/1903.02428v3:PDF  Matthias Fey and Jan Eric Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. http:\/\/arxiv.org\/pdf\/1903.02428v3:PDF"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.5555\/3294771.3294869"},{"key":"e_1_2_1_7_1","volume-title":"Battaglia","author":"Hamrick Jessica B.","year":"2018","unstructured":"Jessica B. Hamrick , Kelsey R. Allen , Victor Bapst , Tina Zhu , Kevin R. McKee , Josh Tenenbaum , and Peter W . Battaglia . 2018 . Relational inductive bias for physical construction in humans and machines. In CogSci . Jessica B. Hamrick, Kelsey R. Allen, Victor Bapst, Tina Zhu, Kevin R. McKee, Josh Tenenbaum, and Peter W. Battaglia. 2018. Relational inductive bias for physical construction in humans and machines. In CogSci."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2588555.2610495"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.14778\/3099622.3099626"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4684-2001-2_9"},{"key":"e_1_2_1_12_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2017","unstructured":"Thomas N. Kipf and Max Welling . 2017 . Semi-Supervised Classification with Graph Convolutional Networks. In ICLR. Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR."},{"key":"e_1_2_1_13_1","first-page":"157","article-title":"Scaling Word2Vec on Big Corpus. Data Sci","volume":"2","author":"Li Bofang","year":"2019","unstructured":"Bofang Li , Aleksandr Drozd , Yuhe Guo , Tao Liu , Satoshi Matsuoka , and Xiaoyong Du . 2019 . Scaling Word2Vec on Big Corpus. Data Sci . Eng. 2 , 4 (2019), 157 -- 175 . Bofang Li, Aleksandr Drozd, Yuhe Guo, Tao Liu, Satoshi Matsuoka, and Xiaoyong Du. 2019. Scaling Word2Vec on Big Corpus. Data Sci. Eng. 2, 4 (2019), 157--175.","journal-title":"Eng."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.14778\/2735479.2735484"},{"key":"e_1_2_1_15_1","volume-title":"Yu","author":"Liu Fanzhen","year":"2020","unstructured":"Fanzhen Liu , Shan Xue , Jia Wu , Chuan Zhou , Wenbin Hu , C\u00e9cile Paris , Surya Nepal , Jian Yang , and Philip S . Yu . 2020 . Deep Learning for Community Detection: Progress, Challenges and Opportunities. In IJCAI. 4981--4987. Fanzhen Liu, Shan Xue, Jia Wu, Chuan Zhou, Wenbin Hu, C\u00e9cile Paris, Surya Nepal, Jian Yang, and Philip S. Yu. 2020. Deep Learning for Community Detection: Progress, Challenges and Opportunities. In IJCAI. 4981--4987."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2661829.2661902"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.5555\/2999792.2999959"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313709"},{"key":"e_1_2_1_19_1","volume-title":"Overlapping Community Detection with Graph Neural Networks. CoRR","author":"Shchur Oleksandr","year":"2019","unstructured":"Oleksandr Shchur and Stephan G\u00fcnnemann . 2019. Overlapping Community Detection with Graph Neural Networks. CoRR ( 2019 ). http:\/\/arxiv.org\/abs\/1909.12201 Oleksandr Shchur and Stephan G\u00fcnnemann. 2019. 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