{"type":"ScholarlyArticle","title":"Community-Centric Graph Convolutional Network for Unsupervised Community Detection","authors":[{"name":"Dongxiao He","slug":"dongxiao-he","given":"Dongxiao","family":"He","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin 300350 , China"},{"name":"Yue Song","slug":"yue-song","given":"Yue","family":"Song","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin 300350 , China"},{"name":"Di Jin","slug":"di-jin","given":"Di","family":"Jin","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin 300350 , China"},{"name":"Zhiyong Feng","slug":"zhiyong-feng","given":"Zhiyong","family":"Feng","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin 300350 , China"},{"name":"Binbin Zhang","slug":"binbin-zhang","given":"Binbin","family":"Zhang","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin 300350 , China"},{"name":"Zhizhi Yu","slug":"zhizhi-yu","given":"Zhizhi","family":"Yu","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin 300350 , China"},{"name":"Weixiong Zhang","slug":"weixiong-zhang","given":"Weixiong","family":"Zhang","affiliation":"Department of Computer Science and Engineering , Washington University , St. Louis , MO 63130 , USA"}],"abstract":"Community detection, aiming at partitioning a network into multiple substructures, is practically importance. Graph convolutional network (GCN), a new deep-learning technique, has recently been developed for community detection. Markov Random Fields (MRF) has been combined with GCN in the MRFasGCN method to improve accuracy. However, the existing GCN community-finding methods are semi-supervised, even though community finding is essentially an unsupervised learning problem. We developed a new GCN approach for unsupervised community detection under the framework of Autoencoder. We cast MRFasGCN as an encoder and then derived node community membership in the hidden layer of the encoder. We introduced a community-centric dual decoder to reconstruct network structures and node attributes separately in an unsupervised fashion, for faithful community detection in the input space. We designed a scheme of local enhancement to accommodate nodes to have more common neighbors and similar attributes with similar community memberships. Experimental results on real networks showed that our new method outperformed the best existing methods, showing the effectiveness of the novel decoding mechanism for generating links and attributes together over the commonly used methods for reconstructing links alone.","doi":"10.24963/ijcai.2020/486","url":"https://ijcai.org/proceedings/2020/486","pdf":"https://ijcai.org/proceedings/2020/0486.pdf","markdown":"https://ijcai.org/proceedings/2020/486.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/486","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":486,"pages":{"first":3515,"last":3521},"keywords":["Machine Learning Applications: Networks","Data Mining: Mining Graphs, Semi Structured Data, Complex Data","Multidisciplinary Topics and Applications: Web Analysis of Communities"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25411/file_25411.m3u8","length_sec":265,"provider":"hls"}],"references":[{"position":1,"raw":"[Balasubramanyan and Cohen, 2011] Ramnath Balasub- ramanyan and William W. Cohen. Block-lda: Jointly modeling entity-annotated text and entity-entity links. In Proceedings of the Eleventh SIAM International Conference on Data Mining, 2011.","title":"Block-lda: Jointly modeling entity-annotated text and entity-entity links","authors":"Ramnath Balasubramanyan, William W Cohen","year":"2011","venue":"Proceedings of the Eleventh SIAM International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Blei et al., 2003] David M. Blei, Andrew Y. Ng, Michael I. Jordan, and John Lafferty. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993-1022, 2003.","title":"Latent dirichlet allocation","authors":"David M Blei, Andrew Y Ng, Michael I Jordan, John Lafferty","year":"2003","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen et al., 2018] Zhengdao Chen, Xiang Li, and Joan Bruna. Supervised community detection with line graph neural networks. arXiv preprint arXiv:1705.08415, 2018.","title":"Supervised community detection with line graph neural networks","authors":"Chen","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Danon et al., 2005] Leon Danon, Albert Díaz-Guilera, Jordi Duch, and Alex Arenas. Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment, 2005(09):P09008-P09008, sep 2005.","title":"Comparing community structure identification","authors":"Leon Danon, Albert Díaz-Guilera, Jordi Duch, Alex Arenas","year":"2005","venue":"Journal of Statistical Mechanics: Theory and Experiment","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Falih et al., 2018] Issam Falih, Nistor Grozavu, Rushed Kanawati, and Younès Bennani. Community detection in attributed network. In Companion Proceedings of the The Web Conference 2018, pages 1299-1306, 2018.","title":"Community detection in attributed network","authors":"Issam Falih, Nistor Grozavu, Rushed Kanawati, Younès Bennani","year":"2018","venue":"Companion Proceedings of the The Web Conference 2018","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Fortunato and Hric, 2016] Santo Fortunato and Darko Hric. Community detection in networks: A user guide. Physics Reports, 659:1-44, 07 2016.","title":"Santo Fortunato and Darko Hric. Community detection in networks: A user guide","authors":"Hric Fortunato","year":"2016","venue":"Physics Reports","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Fortunato, 2010] Santo Fortunato. Community detection in graphs. Physics Reports, 486:75 -174, 2010.","title":"Santo Fortunato. Community detection in graphs","authors":"Fortunato","year":"2010","venue":"Physics Reports","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[He and Chan, 2018] T. He and K. C. C. Chan. Misaga: An algorithm for mining interesting subgraphs in attributed graphs. IEEE Transactions on Cybernetics, 48(5):1369- 1382, May 2018.","title":"Misaga: An algorithm for mining interesting subgraphs in attributed graphs","authors":"T He, K C C Chan","year":"2018","venue":"IEEE Transactions on Cybernetics","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Jin et al., 2018] Di Jin, Meng Ge, Liang Yang, Dongxiao He, Longbiao Wang, and Weixiong Zhang. Integrative network embedding via deep joint reconstruction. In Pro- ceedings of the 27th International Joint Conference on Ar- tificial Intelligence, pages 3407-3413, 7 2018.","title":"Integrative network embedding via deep joint reconstruction","authors":"Di Jin, Meng Ge, Liang Yang, Dongxiao He, Longbiao Wang, Weixiong Zhang","year":"2018","venue":"Proceedings of the 27th International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2018/473"},{"position":10,"raw":"[Jin et al., 2019] Di Jin, Ziyang Liu, Weihao Li, Dongxiao He, and Weixiong Zhang. Graph convolutional networks meet markov random fields: Semi-supervised community detection in attribute networks. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence, 2019.","title":"Graph convolutional networks meet markov random fields: Semi-supervised community detection in attribute networks","authors":"Di Jin, Ziyang Liu, Weihao Li, Dongxiao He, Weixiong Zhang","year":"2019","venue":"Proceedings of the 33rd AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Karrer and Newman, 2011] Brian Karrer and M. E. J. New- man. Stochastic blockmodels and community structure in networks. Physical Review E, 83:016107, Jan 2011.","title":"Stochastic blockmodels and community structure in networks","authors":"Brian Karrer, M E J Newman","year":"2011","venue":"Physical Review E","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Kingma and Ba, 2015] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In Proceed- ings of the 3rd International Conference on Learning Rep- resentations, 2015.","title":"Adam: A method for stochastic optimization","authors":"P Diederik, Jimmy Kingma, Ba","year":"2015","venue":"Proceedings of the 3rd International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Kipf and Welling, 2016] Thomas N. Kipf and Max Welling. Variational graph auto-encoders. CoRR, abs/1611.07308, 2016.","title":"Variational graph auto-encoders","authors":"Thomas N Kipf, Max Welling","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Kipf and Welling, 2017] Thomas N. Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Con- ference on Learning Representations, 2017.","title":"Semi-supervised classification with graph convolutional networks","authors":"Thomas N Kipf, Max Welling","year":"2017","venue":"Proceedings of the 5th International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Li et al., 2018] Qimai Li, Zhichao Han, and Xiao-Ming Wu. Deeper insights into graph convolutional networks for semi-supervised learning. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence, pages 3538- 3545, 2018.","title":"Deeper insights into graph convolutional networks for semi-supervised learning","authors":"Li","year":"2018","venue":"Proceedings of the 32nd AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Li et al., 2019] Pei-Zhen Li, Ling Huang, Chang-Dong Wang, and Jian-Huang Lai. Edmot: An edge enhance- ment approach for motif-aware community detection. In Proceedings of the 25th ACM SIGKDD International Con- ference on Knowledge Discovery and Data Mining, page 479-487, 2019.","title":"Edmot: An edge enhancement approach for motif-aware community detection","authors":"Pei-Zhen Li, Ling Huang, Chang-Dong Wang, Jian-Huang Lai","year":"2019","venue":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Liu et al., 2012] Haifeng Liu, Zhaohui Wu, Deng Cai, and Thomas S. Huang. Constrained nonnegative matrix fac- torization for image representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(7):1299- 1311, July 2012.","title":"Constrained nonnegative matrix factorization for image representation","authors":"Haifeng Liu, Zhaohui Wu, Deng Cai, Thomas S Huang","year":"2012","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Monti et al., 2018] Federico Monti, Karl Otness, and Michael M. Bronstein. MOTIFNET: A motif-based graph convolutional network for directed graphs. In 2018 IEEE Data Science Workshop, pages 225-228, 2018.","title":"MOTIFNET: A motif-based graph convolutional network for directed graphs","authors":"Federico Monti, Karl Otness, Michael M Bronstein","year":"2018","venue":"IEEE Data Science Workshop","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Pan et al., 2018] Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, and Chengqi Zhang. Adversarially reg- ularized graph autoencoder for graph embedding. In Pro- ceedings of the 27th International Joint Conference on Ar- tificial Intelligence, pages 2609-2615, 2018.","title":"Adversarially regularized graph autoencoder for graph embedding","authors":"Pan","year":"2018","venue":"Proceedings of the 27th International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2018/362"},{"position":20,"raw":"[Pan et al., 2019] Shirui Pan, Ruiqi Hu, Sai fu Fung, Guodong Long, Jing Jiang, and Chengqi Zhang. Learning graph embedding with adversarial training methods. arXiv preprint arXiv:1901.01250, 2019.","title":"Learning graph embedding with adversarial training methods","authors":"Pan","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Wang et al., 2016] Xiao Wang, Di Jin, Xiaochun Cao, Liang Yang, and Weixiong Zhang. Semantic community identification in large attribute networks. In Proceedings of the 30th AAAI Conference on Artificial Intelligence, 2016.","title":"Semantic community identification in large attribute networks","authors":"Xiao Wang, Di Jin, Xiaochun Cao, Liang Yang, Weixiong Zhang","year":"2016","venue":"Proceedings of the 30th AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Wang et al., 2017] Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, and Shiqiang Yang. Community pre- serving network embedding. In Proceedings of the 31st AAAI Conference on Artificial Intelligence, 2017.","title":"Community preserving network embedding","authors":"Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, Shiqiang Yang","year":"2017","venue":"Proceedings of the 31st AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Yang et al., 2009] Tianbao Yang, Rong Jin, Yun Chi, and Shenghuo Zhu. Combining link and content for commu- nity detection: A discriminative approach. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 927-936. ACM, 2009.","title":"Combining link and content for community detection: A discriminative approach","authors":"Tianbao Yang, Rong Jin, Yun Chi, Shenghuo Zhu","year":"2009","venue":"Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Yang et al., 2016] Liang Yang, Xiaochun Cao, Dongxiao He, Chuan Wang, Xiao Wang, and Weixiong Zhang. Mod- ularity based community detection with deep learning. In Proceedings of the 25th International Joint Conference on Artificial Intelligence, pages 2252-2258, 2016.","title":"Modularity based community detection with deep learning","authors":"Liang Yang, Xiaochun Cao, Dongxiao He, Chuan Wang, Xiao Wang, Weixiong Zhang","year":"2016","venue":"Proceedings of the 25th International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2016/321"},{"position":25,"raw":"[Zhang et al., 2018] Ge Zhang, Di Jin, Jian Gao, Pengfei Jiao, Franc ¸oise Fogelman-Soulié, and Xin Huang. Finding communities with hierarchical semantics by distinguish- ing general and specialized topics. In Proceedings of the 27th International Joint Conference on Artificial Intelli- gence, pages 3648-3654, 2018.","title":"Finding communities with hierarchical semantics by distinguishing general and specialized topics","authors":"Ge Zhang, Di Jin, Jian Gao, Pengfei Jiao, Xin Franc ¸oise Fogelman-Soulié, Huang","year":"2018","venue":"Proceedings of the 27th International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2018/507"}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}