{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T14:54:26Z","timestamp":1777733666094,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":34,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,10,19]],"date-time":"2020-10-19T00:00:00Z","timestamp":1603065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Natural Science Foundation of China","award":["U1936213"],"award-info":[{"award-number":["U1936213"]}]},{"name":"National Natural Science Foundation of China","award":["U1636207"],"award-info":[{"award-number":["U1636207"]}]},{"name":"NSF","award":["IIS-1763365"],"award-info":[{"award-number":["IIS-1763365"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,10,19]]},"DOI":"10.1145\/3340531.3412042","type":"proceedings-article","created":{"date-parts":[[2020,10,19]],"date-time":"2020-10-19T05:31:03Z","timestamp":1603085463000},"page":"1843-1852","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":57,"title":["CommDGI"],"prefix":"10.1145","author":[{"given":"Tianqi","family":"Zhang","sequence":"first","affiliation":[{"name":"Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Xiong","sequence":"additional","affiliation":[{"name":"Fudan University &amp; Shanghai Institute for Advanced Communication and Data Science, China, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiawei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Florida State University, Tallahassee, FL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yizhu","family":"Jiao","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yangyong","family":"Zhu","sequence":"additional","affiliation":[{"name":"Fudan University &amp; Shanghai Institute for Advanced Communication and Data Science China, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,10,19]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"David Arthur and Sergei Vassilvitskii. 2007. k-means++: the advantages of careful seeding. (2007) 1027--1035.  David Arthur and Sergei Vassilvitskii. 2007. k-means++: the advantages of careful seeding. (2007) 1027--1035."},{"key":"e_1_3_2_2_2_1","volume-title":"MINE: Mutual Information Neural Estimation. arXiv: Learning","author":"Belghazi Mohamed Ishmael","year":"2018","unstructured":"Mohamed Ishmael Belghazi , Sai Rajeswar , Aristide Baratin , Devon R Hjelm , and Aaron Courville . 2018 . MINE: Mutual Information Neural Estimation. arXiv: Learning (2018). Mohamed Ishmael Belghazi, Sai Rajeswar, Aristide Baratin, Devon R Hjelm, and Aaron Courville. 2018. MINE: Mutual Information Neural Estimation. arXiv: Learning (2018)."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1995.7.6.1129"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/2008\/10\/P10008"},{"key":"e_1_3_2_2_5_1","unstructured":"Deyu Bo Xiao Wang Chuan Shi Meiqi Zhu Emiao Lu and Peng Cui. 2020. Structural Deep Clustering Network. arXiv:arXiv:2002.01633  Deyu Bo Xiao Wang Chuan Shi Meiqi Zhu Emiao Lu and Peng Cui. 2020. Structural Deep Clustering Network. arXiv:arXiv:2002.01633"},{"key":"e_1_3_2_2_6_1","volume-title":"Kevin Chenchuan Chang, and Erik Cambria","author":"Cavallari Sandro","year":"2017","unstructured":"Sandro Cavallari , Vincent W Zheng , Hongyun Cai , Kevin Chenchuan Chang, and Erik Cambria . 2017 . Learning Community Embedding with Community Detection and Node Embedding on Graphs . (2017), 377--386. Sandro Cavallari, Vincent W Zheng, Hongyun Cai, Kevin Chenchuan Chang, and Erik Cambria. 2017. Learning Community Embedding with Community Detection and Node Embedding on Graphs. (2017), 377--386."},{"key":"e_1_3_2_2_7_1","unstructured":"Zhengdao Chen Xiang Li and Joan Bruna. 2017. Supervised Community Detection with Line Graph Neural Networks. arXiv:arXiv:1705.08415  Zhengdao Chen Xiang Li and Joan Bruna. 2017. Supervised Community Detection with Line Graph Neural Networks. arXiv:arXiv:1705.08415"},{"key":"e_1_3_2_2_8_1","unstructured":"Xifeng Guo Long Gao Xinwang Liu and Jianping Yin. 2017. Improved Deep Embedded Clustering with Local Structure Preservation. (2017) 1753--1759.  Xifeng Guo Long Gao Xinwang Liu and Jianping Yin. 2017. Improved Deep Embedded Clustering with Local Structure Preservation. (2017) 1753--1759."},{"key":"e_1_3_2_2_9_1","unstructured":"William L Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. (2017) 1024--1034.  William L Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. (2017) 1024--1034."},{"key":"e_1_3_2_2_10_1","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2015. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. (2015) 1026--1034.  Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2015. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. (2015) 1026--1034."},{"key":"e_1_3_2_2_11_1","unstructured":"R Devon Hjelm Alex Fedorov Samuel Lavoiemarchildon Karan Grewal Philip Bachman Adam Trischler and Yoshua Bengio. 2019. Learning deep representations by mutual information estimation and maximization. (2019).  R Devon Hjelm Alex Fedorov Samuel Lavoiemarchildon Karan Grewal Philip Bachman Adam Trischler and Yoshua Bengio. 2019. Learning deep representations by mutual information estimation and maximization. (2019)."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2015.03.075"},{"key":"e_1_3_2_2_13_1","volume-title":"Disentangling by Factorising. arXiv: Machine Learning","author":"Kim Hyunjik","year":"2018","unstructured":"Hyunjik Kim and Andriy Mnih . 2018. Disentangling by Factorising. arXiv: Machine Learning ( 2018 ). Hyunjik Kim and Andriy Mnih. 2018. Disentangling by Factorising. arXiv: Machine Learning (2018)."},{"key":"e_1_3_2_2_14_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling . 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 ( 2016 ). Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_2_15_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2016","unstructured":"Thomas N. Kipf and Max Welling . 2016 . Variational Graph Auto-Encoders . arXiv:arXiv:1611.07308 Thomas N. Kipf and Max Welling. 2016. Variational Graph Auto-Encoders. arXiv:arXiv:1611.07308"},{"key":"e_1_3_2_2_16_1","unstructured":"Ye Li Chaofeng Sha Xin Huang and Yanchun Zhang. 2018. Community Detection in Attributed Graphs: An Embedding Approach. (2018) 338--345.  Ye Li Chaofeng Sha Xin Huang and Yanchun Zhang. 2018. Community Detection in Attributed Graphs: An Embedding Approach. (2018) 338--345."},{"key":"e_1_3_2_2_17_1","unstructured":"Ninghao Liu Qiaoyu Tan Yuening Li Hongxia Yang Jingren Zhou and Xia Hu. 2019. Is a Single Vector Enough?: Exploring Node Polysemy for Network Embedding. (2019) 932--940.  Ninghao Liu Qiaoyu Tan Yuening Li Hongxia Yang Jingren Zhou and Xia Hu. 2019. Is a Single Vector Enough?: Exploring Node Polysemy for Network Embedding. (2019) 932--940."},{"key":"e_1_3_2_2_18_1","unstructured":"Jianxin Ma Peng Cui Kun Kuang Xin Wang and Wenwu Zhu. 2019. Disentangled Graph Convolutional Networks. (2019) 4212--4221.  Jianxin Ma Peng Cui Kun Kuang Xin Wang and Wenwu Zhu. 2019. Disentangled Graph Convolutional Networks. (2019) 4212--4221."},{"key":"e_1_3_2_2_19_1","unstructured":"Tomas Mikolov Ilya Sutskever Kai Chen Greg S Corrado and Jeffrey Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. (2013) 3111--3119.  Tomas Mikolov Ilya Sutskever Kai Chen Greg S Corrado and Jeffrey Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. (2013) 3111--3119."},{"key":"e_1_3_2_2_20_1","unstructured":"Andriy Mnih and Koray Kavukcuoglu. 2013. Learning word embeddings efficiently with noise-contrastive estimation. (2013) 2265--2273.  Andriy Mnih and Koray Kavukcuoglu. 2013. Learning word embeddings efficiently with noise-contrastive estimation. (2013) 2265--2273."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0601602103"},{"key":"e_1_3_2_2_22_1","article-title":"Finding and evaluating community structure in networks","volume":"69","author":"Newman M E J","year":"2004","unstructured":"M E J Newman and Michelle Girvan . 2004 . Finding and evaluating community structure in networks . Physical Review E 69 , 2 (2004), 026113-026113. M E J Newman and Michelle Girvan. 2004. Finding and evaluating community structure in networks. Physical Review E 69, 2 (2004), 026113-026113.","journal-title":"Physical Review E"},{"key":"e_1_3_2_2_23_1","volume-title":"Graph Representation Learning via Graphical Mutual Information Maximization. arXiv: Learning","author":"Peng Zhen","year":"2020","unstructured":"Zhen Peng , Wenbing Huang , Minnan Luo , Qinghua Zheng , Yu Rong , Tingyang Xu , and Junzhou Huang . 2020. Graph Representation Learning via Graphical Mutual Information Maximization. arXiv: Learning ( 2020 ). Zhen Peng, Wenbing Huang, Minnan Luo, Qinghua Zheng, Yu Rong, Tingyang Xu, and Junzhou Huang. 2020. Graph Representation Learning via Graphical Mutual Information Maximization. arXiv: Learning (2020)."},{"key":"e_1_3_2_2_24_1","unstructured":"Bryan Perozzi Rami Alrfou and Steven Skiena. 2014. DeepWalk: online learning of social representations. (2014) 701--710.  Bryan Perozzi Rami Alrfou and Steven Skiena. 2014. DeepWalk: online learning of social representations. (2014) 701--710."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"crossref","unstructured":"Yiye Ruan David Fuhry and Srinivasan Parthasarathy. 2013. Efficient community detection in large networks using content and links. (2013) 1089--1098.  Yiye Ruan David Fuhry and Srinivasan Parthasarathy. 2013. Efficient community detection in large networks using content and links. (2013) 1089--1098.","DOI":"10.1145\/2488388.2488483"},{"key":"e_1_3_2_2_26_1","unstructured":"Fanyun Sun Meng Qu Jordan Hoffmann Chinwei Huang and Jian Tang. 2019. vGraph: A Generative Model for Joint Community Detection and Node Representation Learning. (2019) 514--524.  Fanyun Sun Meng Qu Jordan Hoffmann Chinwei Huang and Jian Tang. 2019. vGraph: A Generative Model for Joint Community Detection and Node Representation Learning. (2019) 514--524."},{"key":"e_1_3_2_2_27_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Petar","year":"2017","unstructured":"Petar Veli?kovi?, Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Lio , and Yoshua Bengio . 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 ( 2017 ). Petar Veli?kovi?, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"e_1_3_2_2_28_1","unstructured":"Petar Velickovic William Fedus William L Hamilton Pietro Lio Yoshua Bengio and R Devon Hjelm. 2019. Deep Graph Infomax. (2019).  Petar Velickovic William Fedus William L Hamilton Pietro Lio Yoshua Bengio and R Devon Hjelm. 2019. Deep Graph Infomax. (2019)."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"crossref","unstructured":"Chun Wang Shirui Pan Ruiqi Hu Guodong Long Jing Jiang and Chengqi Zhang. 2019. Attributed Graph Clustering: A Deep Attentional Embedding Approach. (2019) 3670--3676.  Chun Wang Shirui Pan Ruiqi Hu Guodong Long Jing Jiang and Chengqi Zhang. 2019. Attributed Graph Clustering: A Deep Attentional Embedding Approach. (2019) 3670--3676.","DOI":"10.24963\/ijcai.2019\/509"},{"key":"e_1_3_2_2_30_1","volume-title":"End to end learning and optimization on graphs. arXiv: Learning","author":"Wilder Bryan","year":"2019","unstructured":"Bryan Wilder , Eric Ewing , Bistra Dilkina , and Milind Tambe . 2019. End to end learning and optimization on graphs. arXiv: Learning ( 2019 ). Bryan Wilder, Eric Ewing, Bistra Dilkina, and Milind Tambe. 2019. End to end learning and optimization on graphs. arXiv: Learning (2019)."},{"key":"e_1_3_2_2_31_1","unstructured":"Junyuan Xie Ross Girshick and Ali Farhadi. 2016. Unsupervised deep embedding for clustering analysis. (2016) 478--487.  Junyuan Xie Ross Girshick and Ali Farhadi. 2016. Unsupervised deep embedding for clustering analysis. (2016) 478--487."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-013-0693-z"},{"key":"e_1_3_2_2_33_1","unstructured":"Muhan Zhang and Yixin Chen. 2018. Link Prediction Based on Graph Neural Networks. arXiv:arXiv:1802.09691  Muhan Zhang and Yixin Chen. 2018. Link Prediction Based on Graph Neural Networks. arXiv:arXiv:1802.09691"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"crossref","unstructured":"Xiaotong Zhang Han Liu Qimai Li and Xiaoming Wu. 2019. Attributed Graph Clustering via Adaptive Graph Convolution. (2019) 4327--4333.  Xiaotong Zhang Han Liu Qimai Li and Xiaoming Wu. 2019. Attributed Graph Clustering via Adaptive Graph Convolution. (2019) 4327--4333.","DOI":"10.24963\/ijcai.2019\/601"}],"event":{"name":"CIKM '20: The 29th ACM International Conference on Information and Knowledge Management","location":"Virtual Event Ireland","acronym":"CIKM '20","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 29th ACM International Conference on Information &amp; Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3340531.3412042","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3340531.3412042","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:02:29Z","timestamp":1750197749000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3340531.3412042"}},"subtitle":["Community Detection Oriented Deep Graph Infomax"],"short-title":[],"issued":{"date-parts":[[2020,10,19]]},"references-count":34,"alternative-id":["10.1145\/3340531.3412042","10.1145\/3340531"],"URL":"https:\/\/doi.org\/10.1145\/3340531.3412042","relation":{},"subject":[],"published":{"date-parts":[[2020,10,19]]},"assertion":[{"value":"2020-10-19","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}