Contributed by Zhizhi Yu, Luzhi Wang, Renbiao Wang and Yingli Gong.

1. Deep Learning for Community Detection: Progress, Challenges and Opportunities. IJCAI 2020. paper

   Fanzhen Liu, Shan Xue, Jia Wu, Chuan Zhou, Wenbin Hu, Cécile Paris, Surya Nepal, Jian Yang and Philip S. Yu.

2. CDLIB: a python library to extract, compare and evaluate communities from complex networks. Appl. Netw. Sci. 2019. paper

   Giulio Rossetti, Letizia Milli, Rémy Cazabet.

3. Tracking community evolution in social networks: A survey. Inf. Process. Manag. 2019. paper

   Narimene Dakiche, Fatima Benbouzid-Si Tayeb, Yahya Slimani and Karima Benatchba.

4. Evolutionary Computation for Community Detection in Networks: A Review. IEEE Trans. Evol. Comput. 2018. paper

   Clara Pizzuti.

5. Community Detection and Stochastic Block Models: Recent Developments. J. Mach. Learn. Res. 2017. paper

   Emmanuel Abbe.

6. Metrics for Community Analysis: A Survey. ACM Comput. Surv. 2017. paper

   Tanmoy Chakraborty, Ayushi Dalmia, Animesh Mukherjee and Niloy Ganguly.

7. Clustering Evolving Networks. Algorithm Engineering 2016. paper

   Tanja Hartmann, Andrea Kappes, Dorothea Wagner.

8. Community detection in networks: A user guide. arXiv 2016. paper

   Santo Fortunato, Darko Hric.

9. Community detection in large-scale networks: a survey and empirical evaluation. WIREs Computational Statistics 2014. paper

   Steve Harenberg, Gonzalo Bello, L. Gjeltema, Stephen Ranshous, Jitendra Harlalka, Ramona Seay, Kanchana Padmanabhan and Nagiza Samatova.

10. Evolutionary Network Analysis: A Survey. ACM Comput. Surv. 2014. paper

    Charu C. Aggarwal, Karthik Subbian.

11. Clustering and Community Detection in Directed Networks: A Survey. Physics Reports 2013. paper

    Fragkiskos D. Malliaros，Michalis Vazirgiannis.

12. Overlapping community detection in networks: The state-of-the-art and comparative study. ACM Comput. Surv. 2013. paper

    Jierui Xie, Stephen Kelley, Boleslaw K. Szymanski.

13. Clustering and Community Detection in Directed Networks: A Survey. arXiv 2013. paper

    Fragkiskos D. Malliaros, Michalis Vazirgiannis.

14. A Survey of Community Detection in Online Social Network. IJESTR 2013. paper

    B. Padma Priya, K. Sathiyakumari.

15. Overview of Community Detection Models on Statistical Inference. Computer Science 2012. paper

    Bianfang Chai, Caiyan Jia, Jian Yu.

1. Stochastic blockmodels: First steps Social Networks 1983. paper

   Paul W. Holland, Kathryn Blackmond Laskey, Samuel Leinhardt.

1. Scalable MCMC in degree corrected stochastic block model. ICASSP 2019. paper

   Soumyasundar Pal, Mark Coates.

2. Copula mixed-membership stochastic block model. IJCAI 2016. paper

   Xuhui Fan, Richard Yi Da Xu, Longbing Cao.

3. Mixed membership stochastic blockmodels. J. Mach. Learn. Res. 2008. paper

   Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg and Eric P. Xing.

1. Convexified modularity maximization for degree-corrected stochastic block models. Annals of Statistics 2018. paper

   Yudong Chen, Xiaodong Li, Jiaming Xu.

2. A spectral method for community detection in moderately sparse degree-corrected stochastic block models. Advances in Applied Probability 2017. paper

   Lennart Gulikers, Marc Lelarge, Laurent Massoulié.

3. Consistency of community detection in networks under degree-corrected stochastic block models. Annals of Statistics 2012. paper

   Yunpeng Zhao, Elizaveta Levina, Ji Zhu.

1. Fragmentation Coagulation Based Mixed Membership Stochastic Blockmodel. AAAI 2020. paper

   Zheng Yu, Xuhui Fan, Marcin Pietrasik and Marek Z. Reformat.

2. Change point estimation in a dynamic stochastic block model. J. Mach. Learn. Res. 2020. paper

   Monika Bhattacharjee, Moulinath Banerjee, George Michailidis.

3. Dynamic Stochastic Block Model with Scale-Free Characteristic for Temporal Complex Networks. DASFAA 2019. paper

   Xunxun Wu, Pengfei Jiao, Yaping Wang, Tianpeng Li, Wenjun Wang and Bo Wang.

4. Modeling and detecting change in temporal networks via a dynamic degree corrected stochastic block model. arXiv 2016. paper

   James D. Wilson, Nathaniel T. Stevens, William H. Woodall.

5. Stochastic Block Transition Models for Dynamic Networks. AISTATS 2015. paper

   Kevin S. Xu.

6. Detecting social media hidden communities using dynamic stochastic blockmodel with temporal dirichlet process. TIST 2014. paper

   Xuning Tang, Christopher C. Yang.

7. Dynamic stochastic blockmodels for time-evolving social networks. IEEE J. Sel. Top. Signal Process. 2014. paper

   Kevin S Xu, Alfred Hero.

8. Detecting communities and their evolutions in dynamic social networks - a Bayesian approach. Machine Learning 2011. paper

   Tianbao Yang, Yun Chi, Shenghuo Zhu, Yihong Gong and Rong Jin.

9. Dynamic mixed membership blockmodel for evolving networks. ICML 2009. paper

   Wenjie Fu, Le Song, Eric P. Xing.

10. A state-space mixed membership blockmodel for dynamic network tomography. arXiv 2008. paper

    Eric P Xing, Wenjie Fu, Le Song.

1. Modeling with Node Popularities for Autonomous Overlapping Community Detection. TIST 2020. paper

   Di Jin, Bingyi Li, Pengfei Jiao, Dongxiao He, Hongyu Shan and Weixiong Zhang.

2. Small-Variance Asymptotics for Nonparametric Bayesian Overlapping Stochastic Blockmodels. IJCAI 2018. paper

   Gundeep Arora, Anupreet Porwal, Kanupriya Agarwal, Avani Samdariya and Piyush Rai.

3. Overlapping stochastic block models with application to the french political blogosphere. The Annals of Applied Statistics 2011. paper

   Pierre Latouche，Etienne Birmelé，Christophe Ambroise.

1. A Stochastic Model for Detecting Heterogeneous Link Communities in Complex Networks. AAAI 2015. paper

   Dongxiao He, Dayou Liu, Di Jin and Weixiong Zhang.

1. Stochastic Blockmodels meet Graph Neural Networks. ICML 2019. paper

   Nikhil Mehta, Lawrence Carin, Piyush Rai.

1. An LDA-based Community Structure Discovery Approach for Large-Scale Social Networks. ISI 2007. paper

   Haizheng Zhang, Baojun Qiu, C. Lee Giles, Henry C. Foley and John Yen.

1. Latent Community Topic Analysis: Integration of Community Discovery with Topic Modeling. TIST 2012. paper

   Zhijun Yin, Liangliang Cao, Quanquan Gu and Jiawei Han.

2. Social-network analysis using topic models. SIGIR 2012. paper

   Youngchul Cha, Junghoo Cho.

1. Detecting Communities with Multiplex Semantics by Distinguishing Background, General and Specialized Topics. TKDE 2020. paper

   Di Jin, Kunzeng Wang, Ge Zhang, Pengfei Jiao, Dongxiao He, Françoise Fogelman-Soulié and Xin Huang.

2. Joint identification of network communities and semantics via integrative modeling of network topologies and node contents. AAAI 2017.

   Dongxiao He, Zhiyong Feng, Di Jin, Xiaobao Wang and Weixiong Zhang.

3. A model-based approach to attributed graph clustering. SIGMOD 2012. paper

   Zhiqiang Xu, Yiping Ke, Yi Wang, Hong Cheng and James Cheng.

1. A Novel Generative Topic Embedding Model by Introducing Network Communities. WWW 2019. paper

   Di Jin, Jiantao Huang, Pengfei Jiao, Liang Yang, Dongxiao He, Françoise Fogelman-Soulié and Yuxiao Huang.

2. Efficient Correlated Topic Modeling with Topic Embedding. KDD 2017. paper

   Junxian He, Zhiting Hu, Taylor Berg-Kirkpatrick, Ying Huang and Eric P. Xing.

1. A Non-negative Symmetric Encoder-Decoder Approach for Community Detection. CIKM 2017. paper

   Bingjie Sun, Huawei Shen, Jinhua Gao, Wentao Ouyang and Xueqi Cheng.

2. Community Detection in Social Network with Pairwisely Constrained Symmetric Non-Negative Matrix Factorization. ASONAM 2015. paper

   Xiaohua Shi, Hongtao Lu, Yangcheng He and Shan He.

3. Symmetric Nonnegative Matrix Factorization for Graph Clustering. SIAM 2012. paper

   Da Kuang, Haesun Park, Chris H. Q. Ding.

1. Modeling the Homophily Effect between Links and Communities for Overlapping Community Detection. IJCAI 2016. paper

   Hongyi Zhang, Tong Zhao, Irwin King and Michael R. Lyu.

2. Incorporating Implicit Link Preference Into Overlapping Community Detection. AAAI 2015. paper

   Hongyi Zhang, Irwin King, Michael R. Lyu.

3. Overlapping community detection at scale: a nonnegative matrix factorization approach. WSDM 2013. paper

   Jaewon Yang, Jure Leskovec.

4. Identifying overlapping communities as well as hubs and outliers via nonnegative matrix factorization. Scientific Reports 2013. paper

   Xiaochun Cao, Xiao Wang, Di Jin, Yixin Cao and Dongxiao He.

5. Community discovery using nonnegative matrix factorization. DMKD 2011. paper

   Fei Wang, Tao Li, Xin Wang, Shenghuo Zhu and Chris H. Q. Ding.

1. Semantic Community Identification in Large Attribute Networks. AAAI 2016. paper

   Xiao Wang, Di Jin, Xiaochun Cao, Liang Yang and Weixiong Zhang.

2. Nonnegative Matrix Tri-Factorization with Graph Regularization for Community Detection in Social Networks. IJCAI 2015. paper

   Yulong Pei, Nilanjan Chakraborty, Katia P. Sycara.

1. Evolutionary Nonnegative Matrix Factorization Algorithms for Community Detection in Dynamic Networks. TKDE 2017. paper

   Xiaoke Ma, Di Dong.

2. Autonomous overlapping community detection in temporal networks: A dynamic Bayesian nonnegative matrix factorization approach. KBS 2016. paper

   Wenjun Wang, Pengfei Jiao, Dongxiao He, Di Jin, Lin Pan and Bogdan Gabrys.

1. A Unified Semi-Supervised Community Detection Framework Using Latent Space Graph Regularization. AAAI 2019. paper

   Liang Yang, Xiaochun Cao, Di Jin, Xiao Wang and Dan Meng.

2. Semi-supervised community detection based on non-negative matrix factorization with node popularity. Inf. Sci. 2017. paper

   Xiao Liu, Wenjun Wang, Dongxiao He, Pengfei Jiao, Di Jin and Carlo Vittorio Cannistraci.

1. ModMRF: A modularity-based Markov Random Field method for community detection. Neurocomputing 2020. paper

   Di Jin, Binbin Zhang, Yue Song, Dongxiao He, Zhiyong Feng, Shizhan Chen, Weihao Li and Katarzyna Musial.

2. Incorporating Network Embedding into Markov Random Field for Better Community Detection. AAAI 2019. paper

   Di Jin, Xinxin You, Weihao Li, Dongxiao He, Peng Cui, Françoise Fogelman-Soulié and Tanmoy Chakraborty.

3. A Network-Specific Markov Random Field Approach to Community Detection. AAAI 2018. paper

   Dongxiao He, Xinxin You, Zhiyong Feng, Di Jin, Xue Yang and Weixiong Zhang.

1. An End-to-End Community Detection Model: Integrating LDA into Markov Random Field via Factor Graph. IJCAI 2019. paper

   Dongxiao He, Wenze Song, Di Jin, Zhiyong Feng and Yuxiao Huang.

2. A Novel Ego-Centered Academic Community Detection Approach via Factor Graph Model. IDEAL 2014. paper

   Yusheng Jia, Yang Gao, Wanqi Yang, Jing Huo and Yinghuan Shi.

3. Social Community Analysis via a Factor Graph Model. IEEE Intell. Syst. 2011. paper

   Zi Yang, Jie Tang, Juanzi Li and Wenjun Yang.

1. Network embedding for community detection in attributed networks. ACM TKDD 2020. paper

   Heli Sun, Fang He, Jianbin Huang, Yizhou Sun, Yang Li, Chenyu Wang, Liang He, Zhongbin Sun and Xiaolin Jia.

2. High-performance community detection in social networks using a deep transitive autoencoder. Inf. Sci 2019. paper

   Ying Xie, Xinmei Wang, Dan Jiang and Rongbin Xu.

3. A distributed overlapping community detection model for large graphs using autoencoder. Future Gener. Comput. Syst. 2019. paper

   Vandana Bhatia, Rinkle Rani.

4. Incorporating network structure with node contents for community detection on large networks using deep learning. Neurocomputing 2018. paper

   Jinxin Cao, Di Jin, Liang Yang and Jianwu Dang.

5. Autoencoder based community detection with adaptive integration of network topology and node contents. KSEM 2018. paper

   Jinxin Cao, Di Jin, Jianwu Dang.

6. Using deep learning for community discovery in social networks. ICTAI 2017. paper

   Di Jin, Meng Ge, Zhixuan Li, Wenhuan Lu, Dongxiao He and Françoise Fogelman-Soulié.

7. Modularity based community detection with deep learning. IJCAI 2016. paper

   Liang Yang, Xiaochun Cao, Dongxiao He, Chuan Wang, Xiao Wang and Weixiong Zhang.

1. Stacked autoencoderbased community detection method via an ensemble clustering framework. Inf. Sci 2020. paper

   Rongbin Xu, Yan Che, Xinmei Wang, Jianxiong Hu and Ying Xie.

2. Dfuzzy: a deep learning-based fuzzy clustering model for large graphs. Knowl. Inf. Syst. 2018. paper

   Vandana Bhatia, Rinkle Rani.

3. Learning deep representations for graph clustering. AAAI 2014. paper

   Fei Tian, Bin Gao, Qing Cui, Enhong Chen and Tie-Yan Liu.

1. MGAE: marginalized graph autoencoder for graph clustering. CIKM 2017. paper

   Chun Wang, Shirui Pan, Guodong Long, Xingquan Zhu and Jing Jiang.

2. Graph clustering with dynamic embedding. arXiv 2017. paper

   Carl Yang, Mengxiong Liu, Zongyi Wang, Liyuan Liu and Jiawei Han.

1. Network-specific variational auto-encoder for embedding in attribute networks. IJCAI 2019. paper

   Di Jin, Bingyi Li, Pengfei Jiao, Dongxiao He and Weixiong Zhang.

2. Optimizing variational graph autoencoder for community detection. BigData 2019. paper

   Jun Jin Choong, Xin Liu, Tsuyoshi Murata.

3. Learning community structure with variational autoencoder. ICDM 2018. paper

   Jun Jin Choong, Xin Liu, Tsuyoshi Murata.

4. Adversarially regularized graph autoencoder for graph embedding. IJCAI 2018. paper

   Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao and Chengqi Zhang.

1. SEAL: learning heuristics for community detection with generative adversarial networks. KDD 2020. paper

   Yao Zhang, Yun Xiong, Yun Ye, Tengfei Liu, Weiqiang Wang, Yangyong Zhu and Philip S. Yu

2. JANE: jointly adversarial network embedding. IJCAI 2020. paper

   Liang Yang, Yuexue Wang, Junhua Gu, Chuan Wang, Xiaochun Cao and Yuanfang Guo.

3. Adversarial mutual information learning for network embedding. IJCAI 2020. paper

   Dongxiao He, Lu Zhai, Zhigang Li, Di Jin, Liang Yang, Yuxiao Huang and Philip S. Yu

4. Adversarial attack on community detection by hiding individuals. WWW 2020. paper

   Jia Li, Honglei Zhang, Zhichao Han, Yu Rong, Hong Cheng and Junzhou Huang.

5. CommunityGAN: Community detection with generative adversarial nets. WWW 2019. paper

   Yuting Jia, Qinqin Zhang, Weinan Zhang and Xinbing Wang.

1. Community-centric graph convolutional network for unsupervised community detection. IJCAI 2020. paper

   Dongxiao He, Yue Song, Di Jin, Zhiyong Feng, Binbin Zhang, Zhizhi Yu and Weixiong Zhang.

2. Community detection via joint graph convolutional network embedding in attribute network. ICANN (Workshop) 2019. paper

   Di Jin, Bingyi Li, Pengfei Jiao, Dongxiao He and Hongyu Shan.

3. Heterogeneous graph convolutional networks for temporal community detection. arXiv 2019. paper

   Yaping Zheng, Shiyi Chen, Xiaofeng Zhang and Di Wang.

1. GMNN: graph markov neural networks. ICML 2019. paper

   Meng Qu, Yoshua Bengio, Jian Tang.

2. Graph convolutional networks meet markov random fields: Semi-supervised community detection in attribute networks. AAAI 2019. paper

   Di Jin, Ziyang Liu, Weihao Li, Dongxiao He and Weixiong Zhang.

3. Conditional random field enhanced graph convolutional neural networks. KDD 2019. paper

   Hongchang Gao, Jian Pei, Heng Huang.

1. Graph Convolutional Networks with Markov Random Field Reasoning for Social Spammer Detection. AAAI 2020. paper

   Yongji Wu, Defu Lian, Yiheng Xu, Le Wu and Enhong Chen.

2. Robust Detection of Link Communities in Large Social Networks by Exploiting Link Semantics. AAAI 2018.

   Di Jin, Xiaobao Wang, Ruifang He, Dongxiao He, Jianwu Dang and Weixiong Zhang.

1. Multiscale Community Detection in Functional Brain Networks Constructed Using Dynamic Time Warping. IEEE T. Neur. Sys. Reh. 2020. paper

   Di Jin, Rui Li, Junhai Xu.

2. Community-preserving Graph Convolutions for Structural and Functional Joint Embedding of Brain Networks. BigData 2019. paper

   Jiahao Liu, Guixiang Ma, Fei Jiang, Chun-Ta Lu, Philip S. Yu and Ann B. Ragin.

1. SimClusters: Community-Based Representations for Heterogeneous Recommendations at Twitter. KDD 2020. paper

   Venu Satuluri, Yao Wu, Xun Zheng, Yilei Qian, Brian Wichers, Qieyun Dai, Gui Ming Tang, Jerry Jiang and Jimmy Lin.

2. Towards Recommendation Using Interest-Based Communities in Attributed Social Networks. WWW 2018. paper

   Amani H. B. Eissa, Mohamed E. El-Sharkawi, Hoda M. O. Mokhtar.

3. Experience-Aware Item Recommendation in Evolving Review Communities. ICDM 2015. paper

   Subhabrata Mukherjee, Hemank Lamba, Gerhard Weikum.

1. On Learning Mixed Community-specific Similarity Metrics for Cold-start Link Prediction. WWW 2017. paper

   Linchuan Xu, Xiaokai Wei, Jiannong Cao and Philip S. Yu.

2. Discriminative Link Prediction using Local, Community, and Global Signals. TKDE 2016. paper

   Abir De, Sourangshu Bhattacharya, Sourav Sarkar, Niloy Ganguly and Soumen Chakrabarti.