{"type":"ScholarlyArticle","title":"Deep Learning for Community Detection: Progress, Challenges and Opportunities","authors":[{"name":"Fanzhen Liu","slug":"fanzhen-liu","given":"Fanzhen","family":"Liu","affiliation":"Department of Computing , Macquarie University , Sydney , Australia"},{"name":"Shan Xue","slug":"shan-xue","given":"Shan","family":"Xue","affiliation":"Department of Computing , Macquarie University , Sydney , Australia; CSIRO's Data61 , Sydney , Australia"},{"name":"Jia Wu","slug":"jia-wu","given":"Jia","family":"Wu","affiliation":"Department of Computing , Macquarie University , Sydney , Australia"},{"name":"Chuan Zhou","slug":"chuan-zhou","given":"Chuan","family":"Zhou","affiliation":"Academy of Mathematics and Systems Science , Chinese Academy of Sciences , Beijing , China"},{"name":"Wenbin Hu","slug":"wenbin-hu","given":"Wenbin","family":"Hu","affiliation":"School of Computer Science , Wuhan University , Wuhan , China"},{"name":"Cecile Paris","slug":"cecile-paris","given":"Cecile","family":"Paris","affiliation":"CSIRO's Data61 , Sydney , Australia; Department of Computing , Macquarie University , Sydney , Australia"},{"name":"Surya Nepal","slug":"surya-nepal","given":"Surya","family":"Nepal","affiliation":"CSIRO's Data61 , Sydney , Australia; Department of Computing , Macquarie University , Sydney , Australia"},{"name":"Jian Yang","slug":"jian-yang","given":"Jian","family":"Yang","affiliation":"Department of Computing , Macquarie University , Sydney , Australia"},{"name":"Philip S Yu","slug":"philip-s-yu","given":"Philip S.","family":"Yu","affiliation":"Department of Computer Science , University of Illinois at Chicago , Chicago , USA"}],"abstract":"As communities represent similar opinions, similar functions, similar purposes, etc., community detection is an important and extremely useful tool in both scientific inquiry and data analytics. However, the classic methods of community detection, such as spectral clustering and statistical inference, are falling by the wayside as deep learning techniques demonstrate an increasing capacity to handle high-dimensional graph data with impressive performance. Thus, a survey of current progress in community detection through deep learning is timely. Structured into three broad research streams in this domain – deep neural networks, deep graph embedding, and graph neural networks, this article summarizes the contributions of the various frameworks, models, and algorithms in each stream along with the current challenges that remain unsolved and the future research opportunities yet to be explored.","doi":"10.24963/ijcai.2020/693","url":"https://ijcai.org/proceedings/2020/693","pdf":"https://ijcai.org/proceedings/2020/0693.pdf","markdown":"https://ijcai.org/proceedings/2020/693.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/693","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":"Survey track","track":"Survey track","number":693,"pages":{"first":4981,"last":4987},"keywords":["Machine Learning: general"],"videos":[],"references":[{"position":1,"raw":"[Bhatia and Rani, 2018] V. Bhatia and R. Rani. DFuzzy: A deep learning-based fuzzy clustering model for large graphs. Knowl. Inf. Syst., 57(1):159-181, 2018.","title":"DFuzzy: A deep learning-based fuzzy clustering model for large graphs","authors":"V Bhatia, R Rani","year":"2018","venue":"Knowl. Inf. Syst","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bhatia and Rani, 2019] V. Bhatia and R. Rani. A distributed over- lapping community detection model for large graphs using au- toencoder. Future Gener. Comput. Syst., 94:16-26, 2019.","title":"A distributed overlapping community detection model for large graphs using autoencoder","authors":"V Bhatia, R Rani","year":"2019","venue":"Future Gener. Comput. Syst","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cao et al., 2018a] J. Cao, D. Jin, and J. Dang. Autoencoder based community detection with adaptive integration of network topol- ogy and node contents. In KSEM, pages 184-196, 2018.","title":"Autoencoder based community detection with adaptive integration of network topology and node contents","authors":"Cao","year":"2018","venue":"KSEM","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cao et al., 2018b] J. Cao, D. Jin, L. Yang, and J. Dang. Incorpo- rating network structure with node contents for community de- tection on large networks using deep learning. Neurocomputing, 297:71-81, 2018.","title":"Incorporating network structure with node contents for community detection on large networks using deep learning","authors":"Cao","year":"2018","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cavallari et al., 2017] S. Cavallari, V. Zheng, H. Cai, K. Chang, and E. Cambria. Learning community embedding with commu- nity detection and node embedding on graphs. In CIKM, page 377-386, 2017.","title":"Learning community embedding with community detection and node embedding on graphs","authors":"S Cavallari, V Zheng, H Cai, K Chang, E Cambria","year":"2017","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chang et al., 2015] S. Chang, W. Han, J. Tang, G. Qi, C. Aggar- wal, and T. Huang. Heterogeneous network embedding via deep architectures. In KDD, pages 119-128, 2015.","title":"Heterogeneous network embedding via deep architectures","authors":"S Chang, W Han, J Tang, G Qi, C Aggarwal, T Huang","year":"2015","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chen and Redner, 2010] P. Chen and S. Redner. Community struc- ture of the physical review citation network. J. Informetr., 4(3):278-290, 2010.","title":"Community structure of the physical review citation network","authors":"P Chen, S Redner","year":"2010","venue":"J. Informetr","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Chen and Yuan, 2006] J. Chen and B. Yuan. Detecting functional modules in the yeast protein-protein interaction network. Bioin- formatics, 22(18):2283-2290, 07 2006.","title":"Detecting functional modules in the yeast protein-protein interaction network","authors":"J Chen, B Yuan","year":"2006","venue":"Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Chen et al., 2019] Z. Chen, L. Li, and J. Bruna. Supervised com- munity detection with line graph neural networks. In ICLR, 2019.","title":"Supervised community detection with line graph neural networks","authors":"Z Chen, L Li, J Bruna","year":"2019","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Choong et al., 2018] J. Choong, X. Liu, and T. Murata. Learn- ing community structure with variational autoencoder. In ICDM, pages 69-78, 2018.","title":"Learning community structure with variational autoencoder","authors":"J Choong, X Liu, T Murata","year":"2018","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Fortunato and Hric, 2016] S. Fortunato and D. Hric. Community detection in networks: A user guide. Phys. Rep., 659:1-44, 2016. Community detection in networks: A user guide.","title":"Community detection in networks: A user guide","authors":"S Fortunato, D Hric","year":"2016","venue":"Phys. Rep","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Fortunato, 2010] S. Fortunato. Community detection in graphs. Phys. Rep., 486(3-5):75-174, 2010.","title":"Community detection in graphs","authors":"S Fortunato","year":"2010","venue":"Phys. Rep","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Fu et al., 2017] T. Fu, W. Lee, and Z. Lei. Hin2vec: Explore meta- paths in heterogeneous information networks for representation learning. In CIKM, page 1797-1806, 2017.","title":"Hin2vec: Explore metapaths in heterogeneous information networks for representation learning","authors":"T Fu, W Lee, Z Lei","year":"2017","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[He et al., 2017] D. He, Z. Feng, D. Jin, X. Wang, and W. Zhang. Joint identification of network communities and semantics via integrative modeling of network topologies and node contents. In AAAI, pages 116-124, 2017.","title":"Joint identification of network communities and semantics via integrative modeling of network topologies and node contents","authors":"D He, Z Feng, D Jin, X Wang, W Zhang","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Hu et al., 2019] B. Hu, H. Wang, X. Yu, W. Yuan, and T. He. Sparse network embedding for community detection and sign prediction in signed social networks. J. Ambient Intell. Hum. Comput., 10(1):175-186, Jan 2019.","title":"Sparse network embedding for community detection and sign prediction in signed social networks","authors":"B Hu, H Wang, X Yu, W Yuan, T He","year":"2019","venue":"J. Ambient Intell. Hum. Comput","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Jia et al., 2019] Y. Jia, Q. Zhang, W. Zhang, and X. Wang. Com- munityGAN: Community detection with generative adversarial nets. In WWW, pages 784-794, 2019.","title":"Com-munityGAN: Community detection with generative adversarial nets","authors":"Y Jia, Q Zhang, W Zhang, X Wang","year":"2019","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Jin et al., 2019] D. Jin, Z. Liu, W. Li, D. He, and W. Zhang. Graph convolutional networks meet markov random fields: Semi- supervised community detection in attribute networks. In AAAI, pages 152-159, 2019.","title":"Graph convolutional networks meet markov random fields: Semisupervised community detection in attribute networks","authors":"D Jin, Z Liu, W Li, D He, W Zhang","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Karrer and Newman, 2011] B. Karrer and M. Newman. Stochastic blockmodels and community structure in networks. Phys. Rev. E, 83:016107, 2011.","title":"Stochastic blockmodels and community structure in networks","authors":"B Karrer, M Newman","year":"2011","venue":"Phys. Rev. E","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[LeCun et al., 2015] Y. LeCun, Y. Bengio, and G. Hinton. Deep learning. Nature, 521:436-444, 2015.","title":"Deep learning","authors":"Y Lecun, Y Bengio, G Hinton","year":"2015","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Li et al., 2018] Y. Li, C. Sha, X. Huang, and Y. Zhang. Community detection in attributed graphs: an embedding approach. In AAAI, pages 338-345, 2018.","title":"Community detection in attributed graphs: an embedding approach","authors":"Y Li, C Sha, X Huang, Y Zhang","year":"2018","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Liu et al., 2020] F. Liu, J. Wu, S. Xue, C. Zhou, J. Yang, and Q. Sheng. Detecting the evolving community structure in dy- namic social networks. World Wide Web, 23:715-733, 2020.","title":"Detecting the evolving community structure in dynamic social networks","authors":"F Liu, J Wu, S Xue, C Zhou, J Yang, Q Sheng","year":"2020","venue":"World Wide Web","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Ng et al., 2002] A. Ng, M. Jordan, and Y. Weiss. On spectral clus- tering: analysis and an algorithm. In NIPS, pages 849-856, 2002.","title":"On spectral clustering: analysis and an algorithm","authors":"A Ng, M Jordan, Y Weiss","year":"2002","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Ngiam et al., 2011] J. Ngiam, Z. Chen, S. Bhaskar, P. Koh, and A. Ng. Sparse filtering. In NIPS, pages 1125-1133, 2011.","title":"Sparse filtering","authors":"J Ngiam, Z Chen, S Bhaskar, P Koh, A Ng","year":"2011","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Rosvall and Bergstrom, 2008] M. Rosvall and C. Bergstrom. Maps of random walks on complex networks reveal community struc- ture. PNAS, 105(4):1118-1123, 2008.","title":"Maps of random walks on complex networks reveal community structure","authors":"M Rosvall, C Bergstrom","year":"2008","venue":"PNAS","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Shchur and Gunnemann, 2019] O. Shchur and S. Gunnemann. Overlapping community detection with graph neural networks. In KDD Workshop DLG'19, 2019.","title":"Overlapping community detection with graph neural networks","authors":"O Shchur, S Gunnemann","year":"2019","venue":"KDD Workshop DLG'19","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Shen and Chung, 2018] X. Shen and F. Chung. Deep network em- bedding for graph representation learning in signed networks. IEEE Trans. Cybern., pages 1-8, 2018.","title":"Deep network embedding for graph representation learning in signed networks","authors":"Chung ; Shen, X Shen, F Chung","year":"2018","venue":"IEEE Trans. Cybern","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Song and Thiagarajan, 2018] H. Song and J. Thiagarajan. Im- proved deep embeddings for inferencing with multi-layered net- works. arXiv:1811.12156, 2018.","title":"Improved deep embeddings for inferencing with multi-layered networks","authors":"H Song, J Thiagarajan","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Sperlí, 2019] G. Sperlí. A deep learning based community detec- tion approach. In SAC, pages 1107-1110, 2019.","title":"A deep learning based community detection approach","authors":"G Sperlí","year":"2019","venue":"SAC","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Sun et al., 2019] F. Sun, M. Qu, J. Hoffmann, C. Huang, and J. Tang. vGraph: A generative model for joint community detec- tion and node representation learning. In NIPS, pages 512-522, 2019.","title":"vGraph: A generative model for joint community detection and node representation learning","authors":"F Sun, M Qu, J Hoffmann, C Huang, J Tang","year":"2019","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Tian et al., 2014] F. Tian, B. Gao, Q. Cui, E. Chen, and T. Liu. Learning deep representations for graph clustering. In AAAI, pages 1293-1299, 2014.","title":"Learning deep representations for graph clustering","authors":"F Tian, B Gao, Q Cui, E Chen, T Liu","year":"2014","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Tu et al., 2019] C. Tu, X. Zeng, H. Wang, Z. Zhang, Z. Liu, M. Sun, B. Zhang, and L. Lin. A unified framework for commu- nity detection and network representation learning. IEEE Trans. Knowl. Data Eng., 31(6):1051-1065, 2019.","title":"A unified framework for community detection and network representation learning","authors":"C Tu, X Zeng, H Wang, Z Zhang, Z Liu, M Sun, B Zhang, L Lin","year":"2019","venue":"IEEE Trans. Knowl. Data Eng","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Wang et al., 2019] H. Wang, J. Wang, J. Wang, M. Zhao, W. Zhang, F. Zhang, W. Li, X. Xie, and M. Guo. Learning graph representation with generative adversarial nets. IEEE Trans. Knowl. Data. Eng., 2019.","title":"Learning graph representation with generative adversarial nets","authors":"H Wang, J Wang, J Wang, M Zhao, W Zhang, F Zhang, W Li, X Xie, M Guo","year":"2019","venue":"IEEE Trans. Knowl. Data. Eng","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Wu et al., 2020] Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu. A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst., 2020.","title":"A comprehensive survey on graph neural networks","authors":"Z Wu, S Pan, F Chen, G Long, C Zhang, P S Yu","year":"2020","venue":"IEEE Trans. Neural Netw. Learn. Syst","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Xie et al., 2018] Y. Xie, M. Gong, S. Wang, and B. Yu. Commu- nity discovery in networks with deep sparse filtering. Pattern Recognit., 81:50-59, 2018.","title":"Community discovery in networks with deep sparse filtering","authors":"Y Xie, M Gong, S Wang, B Yu","year":"2018","venue":"Pattern Recognit","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Xin et al., 2017] X. Xin, C. Wang, X. Ying, and B. Wang. Deep community detection in topologically incomplete networks. Physica A, 469:342-352, 2017.","title":"Deep community detection in topologically incomplete networks","authors":"X Xin, C Wang, X Ying, B Wang","year":"2017","venue":"Physica A","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Xu et al., 2019] P. Xu, W. Hu, J. Wu, and B. Du. Link prediction with signed latent factors in signed social networks. In KDD, page 1046-1054, 2019.","title":"Link prediction with signed latent factors in signed social networks","authors":"P Xu, W Hu, J Wu, B Du","year":"2019","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Xue et al., 2019] S. Xue, J. Lu, and G. Zhang. Cross-domain net- work representations. Pattern Recognit., 94:135-148, 2019.","title":"Cross-domain network representations","authors":"S Xue, J Lu, G Zhang","year":"2019","venue":"Pattern Recognit","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Yang et al., 2013] J. Yang, J. McAuley, and J. Leskovec. Commu- nity detection in networks with node attributes. In ICDM, 2013.","title":"Community detection in networks with node attributes","authors":"J Yang, J Mcauley, J Leskovec","year":"2013","venue":"ICDM","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Yang et al., 2016] L. Yang, X. Cao, D. He, C. Wang, X. Wang, and W. Zhang. Modularity based community detection with deep learning. In IJCAI, pages 2252-2258, 2016.","title":"Modularity based community detection with deep learning","authors":"L Yang, X Cao, D He, C Wang, X Wang, W Zhang","year":"2016","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2016/321"},{"position":40,"raw":"[Ye et al., 2018] F. Ye, C. Chen, and Z. Zheng. Deep autoencoder- like nonnegative matrix factorization for community detection. In CIKM, pages 1393-1402, 2018.","title":"Deep autoencoderlike nonnegative matrix factorization for community detection","authors":"F Ye, C Chen, Z Zheng","year":"2018","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Zhang et al., 2017] J. Zhang, P. S. Yu, and Y. Lv. Enterprise com- munity detection. In ICDE, pages 219-222, 2017.","title":"Enterprise community detection","authors":"J Zhang, P S Yu, Y Lv","year":"2017","venue":"ICDE","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Zhang et al., 2018] Y. Zhang, T. Lyu, and Y. Zhang. Cosine: Community-preserving social network embedding from informa- tion diffusion cascades. In AAAI, pages 2620-2627, 2018.","title":"Cosine: Community-preserving social network embedding from information diffusion cascades","authors":"Y Zhang, T Lyu, Y Zhang","year":"2018","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhang, 2018] X. Zhang. Multilayer bootstrap networks. Neural Networks, 103:29-43, 2018.","title":"Multilayer bootstrap networks","authors":"X Zhang","year":"2018","venue":"Neural Networks","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}