{"type":"ScholarlyArticle","title":"An End-to-End Community Detection Model: Integrating LDA into Markov Random Field via Factor Graph","authors":[{"name":"Dongxiao He","slug":"dongxiao-he","given":"Dongxiao","family":"He","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Wenze Song","slug":"wenze-song","given":"Wenze","family":"Song","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Di Jin","slug":"di-jin","given":"Di","family":"Jin","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Zhiyong Feng","slug":"zhiyong-feng","given":"Zhiyong","family":"Feng","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Yuxiao Huang","slug":"yuxiao-huang","given":"Yuxiao","family":"Huang","affiliation":"Data Science , George Washington University , Washington , D.C. , United States"}],"abstract":"Markov Random Field (MRF) has been successfully used in community detection recently. However, existing MRF methods only utilize the network topology while ignore the semantic attributes. A straightforward way to combine the two types of information is that, one can first use a topic clustering model (e.g. LDA) to derive group membership of nodes by using the semantic attributes, then take this result as a prior to define the MRF model. In this way, however, the parameters of the two models cannot be adjusted by each other, preventing it from really realizing the complementation of the advantages of the two. This paper integrates LDA into MRF to form an end-to-end learning system where their parameters can be trained jointly. However, LDA is a directed graphic model whereas MRF is undirected, making their integration a challenge. To handle this problem, we first transform LDA and MRF into a unified factor graph framework, allowing sharing the parameters of the two models. We then derive an efficient belief propagation algorithm to train their parameters simultaneously, enabling our approach to take advantage of the strength of both LDA and MRF. Empirical results show that our approach compares favorably with the state-of-the-art methods.","doi":"10.24963/ijcai.2019/794","url":"https://ijcai.org/proceedings/2019/794","pdf":"https://ijcai.org/proceedings/2019/0794.pdf","markdown":"https://ijcai.org/proceedings/2019/794.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/794","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Uncertainty in AI","track":"Main track","number":794,"pages":{"first":5730,"last":5736},"keywords":["Uncertainty in AI: Graphical Models","Machine Learning Applications: Networks"],"videos":[],"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 2011 SIAM International Conference on Data Mining, pages 450-461. SIAM, 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 2011 SIAM International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Blake et al., 2011] Andrew Blake, Pushmeet Kohli, and Carsten Rother. Markov random fields for vision and im- age processing. 2011.","title":"Markov random fields for vision and image processing","authors":"Andrew Blake, Pushmeet Kohli, Carsten Rother","year":"2011","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Blei et al., 2003] David M Blei, Andrew Y Ng, and Michael I Jordan. Latent dirichlet allocation. Journal of machine Learning research, 3(Jan):993-1022, 2003.","title":"Latent dirichlet allocation","authors":"Andrew Y David M Blei, Michael I Ng, Jordan","year":"2003","venue":"Journal of machine Learning research","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bojchevski and Günnemann, 2018] Aleksandar Bojchevski and Stephan Günnemann. Bayesian robust attributed graph clustering: Joint learning of partial anomalies and group structure. In Thirty-Second AAAI Conference on Artificial Intelligence, 2018.","title":"Bayesian robust attributed graph clustering: Joint learning of partial anomalies and group structure","authors":"Aleksandar Bojchevski, Stephan Günnemann","year":"2018","venue":"Thirty-Second AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Brian and Newman, 2011] Karrer Brian and M E J New- man. Stochastic blockmodels and community structure in networks. Phys Rev E Stat Nonlin Soft Matter Phys, 83(2):016107, 2011.","title":"Stochastic blockmodels and community structure in networks","authors":"Karrer Brian, M E J Newman","year":"2011","venue":"Phys Rev E Stat Nonlin Soft Matter Phys","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chen and Li, 2010] Dongming Chen and Lili Li. Improved structural algorithm for uncovering community structure in networks. In International Conference on Computer Application & System Modeling, 2010.","title":"Improved structural algorithm for uncovering community structure in networks","authors":"Chen, Li ; ] Dongming Chen, Lili Li","year":"2010","venue":"International Conference on Computer Application & System Modeling","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chorowski et al., 2014] Jan Chorowski, Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. End-to-end continu- ous speech recognition using attention-based recurrent nn: first results. arXiv preprint arXiv:1412.1602, 2014.","title":"End-to-end continuous speech recognition using attention-based recurrent nn: first results","authors":"Chorowski","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Danon et al., 2005] Leon Danon, Albert Diaz-Guilera, Jordi Duch, and Alex Arenas. Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment, 2005(09):P09008, 2005.","title":"Comparing community structure identification","authors":"Leon Danon, Albert Diaz-Guilera, Jordi Duch, Alex Arenas","year":"2005","venue":"Journal of Statistical Mechanics: Theory and Experiment","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Fortunato and Hric, 2016] Santo Fortunato and Darko Hric. Community detection in networks: A user guide. Physics Reports, 659:1-44, 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":10,"raw":"[Girvan and Newman, 2002] Michelle Girvan and Mark EJ Newman. Community structure in social and biological networks. Proceedings of the national academy of sci- ences, 99(12):7821-7826, 2002.","title":"Community structure in social and biological networks","authors":"Newman Girvan, Michelle Girvan, Mark Ej Newman","year":"2002","venue":"Proceedings of the national academy of sciences","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[He et al., 2015] Dongxiao He, Dayou Liu, Di Jin, and Weixiong Zhang. A stochastic model for detecting het- erogeneous link communities in complex networks. In Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015.","title":"A stochastic model for detecting heterogeneous link communities in complex networks","authors":"Dongxiao He, Dayou Liu, Di Jin, Weixiong Zhang","year":"2015","venue":"Twenty-Ninth AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[He et al., 2018] Dongxiao He, Xinxin You, Zhiyong Feng, Di Jin, Xue Yang, and Weixiong Zhang. A network- specific markov random field approach to community de- tection. In Thirty-Second AAAI Conference on Artificial Intelligence, 2018.","title":"A networkspecific markov random field approach to community detection","authors":"Dongxiao He, Xinxin You, Zhiyong Feng, Di Jin, Xue Yang, Weixiong Zhang","year":"2018","venue":"Thirty-Second AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Jin et al., 2019] Di Jin, Xinxin You, Weihao Li, Dongxi- ao He, Peng Cui, Francoise Soulie Fogelman, and Tan- moy Chakraborty. Incorporating network embedding into markov random field for better community detection. In Thirty-Third AAAI Conference on Artificial Intelligence, 2019.","title":"Incorporating network embedding into markov random field for better community detection","authors":"Di Jin, Xinxin You, Weihao Li, Dongxiao He, Peng Cui, Francoise Soulie Fogelman, Tanmoy Chakraborty","year":"2019","venue":"Thirty-Third AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Liu et al., 2012] Haifeng Liu, Zhaohui Wu, Xuelong Li, Deng Cai, and Thomas S Huang. Constrained nonnega- tive matrix factorization for image representation. IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 34(7):1299-1311, 2012.","title":"Constrained nonnegative matrix factorization for image representation","authors":"Haifeng Liu, Zhaohui Wu, Xuelong Li, Deng Cai, Thomas S Huang","year":"2012","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Newman and Girvan, 2004] M E J Newman and . Girvan, M. Finding and evaluating community structure in net- works. Physical Review E Statistical Nonlinear & Soft Matter Physics, 69(2):026113, 2004.","title":"Finding and evaluating community structure in networks","authors":"M E J Newman, M Girvan","year":"2004","venue":"Physical Review E Statistical Nonlinear & Soft Matter Physics","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Rosenberg, 2007] Julia Hirschberg Andrew Rosenberg. Joint conference on empirical methods in natural language processing and computational natural language learning.","title":"Joint conference on empirical methods in natural language processing and computational natural language learning","authors":"Julia Hirschberg, Andrew Rosenberg","year":null,"venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"V-Measure: A conditional entropy-based external clus- ter evaluation measure. Lecture Notes in Artificial Intel- ligence. Prague: Association for Computational Linguis- tics, 2007.","title":"V-Measure: A conditional entropy-based external cluster evaluation measure","authors":null,"year":"2007","venue":"Lecture Notes in Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Ruan et al., 2013] Yiye Ruan, David Fuhry, and Srinivasan Parthasarathy. Efficient community detection in large net- works using content and links. In International Confer- ence on World Wide Web, 2013.","title":"Efficient community detection in large networks using content and links","authors":"Yiye Ruan, David Fuhry, Srinivasan Parthasarathy","year":"2013","venue":"International Conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Tao et al., 2019] Zhiqiang Tao, Hongfu Liu, Sheng Li, Zhengming Ding, and Yun Fu. Marginalized multiview en- semble clustering. IEEE transactions on neural networks and learning systems, 2019.","title":"Marginalized multiview ensemble clustering","authors":"Zhiqiang Tao, Hongfu Liu, Sheng Li, Zhengming Ding, Yun Fu","year":"2019","venue":"IEEE transactions on neural networks and learning systems","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Wang et al., 2016] Xiao Wang, Di Jin, Xiaochun Cao, Liang Yang, and Weixiong Zhang. Semantic communi- ty identification in large attribute networks. In Thirtieth 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":"Thirtieth AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":21,"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 Proceed- ings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 927-936, Paris, France, 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":22,"raw":"[Yedidia et al., 2003] Jonathan S Yedidia, William T Free- man, and Yair Weiss. Understanding belief propagation and its generalizations. Exploring artificial intelligence in the new millennium, 8:236-239, 2003.","title":"Understanding belief propagation and its generalizations","authors":"Jonathan S Yedidia, William T Freeman, Yair Weiss","year":"2003","venue":"Exploring artificial intelligence in the new millennium","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Zeng and Liu, 2008] Jia Zeng and Zhi-Qiang Liu. Markov random field-based statistical character structure model- ing for handwritten chinese character recognition. IEEE transactions on pattern analysis and machine intelligence, 30(5):767-780, 2008.","title":"Markov random field-based statistical character structure modeling for handwritten chinese character recognition","authors":"Jia Zeng, Zhi-Qiang Liu","year":"2008","venue":"IEEE transactions on pattern analysis and machine intelligence","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Zeng et al., 2013] Jia Zeng, William K Cheung, and Jim- ing Liu. Learning topic models by belief propagation. IEEE Transactions on Pattern Analysis and Machine In- telligence, 35(5):1121-1134, 2013.","title":"Learning topic models by belief propagation","authors":"Jia Zeng, William K Cheung, Jiming Liu","year":"2013","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Zhang et al., 2018] Ge Zhang, Di Jin, Jian Gao, Pengfei Jiao, Francoise 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. AAAI Press, 2018.","title":"Finding communities with hierarchical semantics by distinguishing general and specialized topics","authors":"Ge Zhang, Di Jin, Jian Gao, Pengfei Jiao, Francoise Fogelman-Soulié, Xin 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."}