A Survey on the Recent Advances of Deep Community Detection
Source abstract
Abstract
In the first days of social networking, the typical view of a community was a set of user profiles of the same interests and likes, and this community kept enlarging by searching, proposing, and adding new members with the same characteristics that were likely to interfere with the existing members. Today, things have changed dramatically. Social networking platforms are not restricted to forming similar user profiles: The vast amounts of data produced every day have given opportunities to predict and suggest relationships, behaviors, and everyday activities like shopping, food, traveling destinations, etc. Every day, vast data amounts are generated by the famous social networks such as Facebook, Twitter, Instagram, and so on. For example, Facebook alone generates 4 petabytes of data per day. The analysis of such data is of high importance to many aspects like recommendation systems, businesses, health organizations, etc. The community detection problem received considerable attention a long time ago. Communities are represented as clusters of an entire network. Most of the community detection techniques are based on graph structures. In this paper, we present the recent advances of deep learning techniques for community detection. We describe the most recent strategies presented in this field, and we provide some general discussion and some future trends.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Complex Network Analysis Techniques · Network Security and Intrusion Detection · Spam and Phishing Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Applied Informatics, School of Information Sciences, University of Macedonia Thessaloniki, PC 54616 Thessaloniki, Greece
Department of Midwifery, School of Health Sciences, University of Western Macedonia, PC 50200 Ptolemaida, Greece
Department of Production and Management Engineering, Democritus University of Thrace, PC 67100 Xanthi, Greece
Funding
University of Western Macedonia
References (74)
Show source-provided references
1. Liu, F., Xue, S., Wu, J., Zhou, C., Hu, W., Paris, C., Nepal, S., Yang, J., and Yu, P.S. (2021, January 11–17). Deep Learning for Community Detection: Progress, Challenges and Opportunities. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20), Yokohama, Japan.10.24963/ijcai.2020/693 ↗
2. Detecting communities with multiplex semantics by distinguishing background, general and specialized topics10.1109/TKDE.2019.2937298 ↗
3. Satuluri, V., Wu, Y., Zheng, X., Qian, Y., Wichers, B., Dai, Q., Tang, G.M., Jiang, J., and Lin, J. (2020, January 23–27). Simclusters: Community-based representations for heterogeneous recommendations at twitter. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Event, Online.10.1145/3394486.3403370 ↗
4. AD-C: A new node anomaly detection based on community detection in social networks10.1504/IJEB.2020.109069 ↗
5. Convexified modularity maximization for degree-corrected stochastic block models10.1214/17-AOS1595 ↗
6. Multiobjective optimization and local merge for clustering attributed graphs10.1109/TCYB.2018.2889413 ↗
7. A spectral method for community detection in moderately sparse degree-corrected stochastic block models10.1017/apr.2017.18 ↗
8. Dhilber, M., and Bhavani, S.D. (2020, January 9–12). Community Detection in Social Networks Using Deep Learning. Proceedings of the 16th International Conference, ICDCIT 2020, Bhubaneswar, India.
9. A Parallel Algorithm for Community Detection in Social Networks, Based on Path Analysis and Threaded Binary Trees10.1109/ACCESS.2019.2897783 ↗
10. Hybrid CPU-GPU Community Detection in Weighted Networks10.1109/ACCESS.2020.2982227 ↗
11. Jin, D., Yu, Z., Jiao, P., Pan, S., Yu, P.S., and Zhang, W. (2021). A Survey of Community Detection Approaches: From Statistical Modeling to Deep Representation. arXiv.10.1109/TKDE.2021.3104155 ↗
12. Leskovec, J., and Krevl, A. (2014). SNAP Datasets: Stanford Large Network Dataset Collection, University Stanford. Available online: http://snap.stanford.edu/data.
13. (2021, July 26). Citation Network Dataset: DBLP+Citation, ACM Citation Network. Available online: https://www.aminer.org/citation.
14. (2012, May 08). Email-EU. Available online: https://paperswithcode.com/dataset/email-eu.
15. Algorithms and applications for community detection in weighted networks10.1109/TPDS.2014.2370031 ↗
16. A fast parallel community discovery model on complex networks through approximate optimization10.1109/TKDE.2018.2803818 ↗
17. Matin, P., and Zhan, J. (2018, January 9–11). Optimized Label Propagation Community Detectionon Big Data Networks. Proceedings of the 2018 International Conference on Big Data and Education(ICBDE’18), Honolulu, HI, USA.
18. Ascent–descent variable neigh-borhood decomposition search for community detection by modularity maximization
19. Fan, S., Wang, X., Shi, C., Lu, E., Lin, K., and Wang, B. (2020, January 20–24). One2Multi Graph Autoencoder for Multi-view Graph Clustering. Proceedings of the Web Conference 2020 (WWW ’20), Taipei, Taiwan.10.1145/3366423.3380079 ↗
20. Luo, D., Ni, J., Wang, S., Bian, Y., Yu, X., and Zhang, X. (2020, January 3–7). Deep Multi-Graph Clustering via Attentive Cross-Graph Association. Proceedings of the 13th International Conference on Web Search and Data Mining, (WSDM 2020), Houston, TX, USA.10.1145/3336191.3371806 ↗
21. Sarkar, A., Mehta, N., and Rai, P. (2020, January 7–12). Graph Representation Learning via Ladder Gamma Variational Autoencoders. Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), New York, NY, USA.
22. Shi, H., Fan, H., and Kwok, J.T. (2020, January 7–12). Effective Decoding in Graph Auto-Encoder Using Triadic Closure. Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), New York, NY, USA.
23. Community detection algorithm based on deep sparse autoencoder
24. Deep Learning Techniques for Community Detection in Social Networks10.1109/ACCESS.2020.2996001 ↗
25. Cao, J., Jin, D., and Dang, J. (2018, January 17–19). Auto-encoder based community detection with adaptive integration of network topology and node contents. Proceedings of the International Conference on Knowledge Science, Engineering and Management, Changchun, China.10.1007/978-3-319-99247-1_16 ↗
26. Incorporating network structure with node contents for community detection on large networks using deep learning10.1016/j.neucom.2018.01.065 ↗
27. A distributed overlapping community detection model for large graphs using autoencoder10.1016/j.future.2018.10.045 ↗
28. Shao, M., Li, S., Ding, Z., and Fu, Y. (2021, January 13–15). Deep linear coding for fast graph clustering. Proceedings of the 24th International Conference on Artificial Intelligence, Virtually, Online.
29. Song, C., Liu, F., Huang, Y., Wang, L., and Tan, T. (2013). Auto-encoder based data clustering. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, Springer.10.1007/978-3-642-41822-8_15 ↗
30. Tian, F., Gao, B., Cui, Q., Chen, E., and Liu, T.-Y. (2014, January 27–31). Learning deep representations for graph clustering. Proceedings of the 28th AAAI Conference on Artificial Intelligence, Québec City, QC, Canada.10.1609/aaai.v28i1.8916 ↗
31. Deep learning10.1038/nature14539 ↗
32. Cavallari, S., Zheng, V.W., Cai, H., Chang, K.C.-C., and Cambria, E. (2017, January 6–10). Learning community embedding with community detection and node embedding on graphs. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, Singapore.10.1145/3132847.3132925 ↗
33. Yu, W., Zheng, C., Cheng, W., Aggarwal, C.C., Song, D., Zong, B., Chen, H., and Wang, W. (2018, January 19–23). Learning deep network representations with adversarially regularized autoencoders. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK.10.1145/3219819.3220000 ↗
34. Stacked auto-encoder based community detection method via an ensemble clustering framework10.1016/j.ins.2020.03.090 ↗
35. Revealing community structures by ensemble clustering using group diffusion10.1016/j.inffus.2017.09.013 ↗
36. Choong, J.J., Liu, X., and Murata, T. (2020). Optimizing Variational Graph Autoencoder for Community Detection with Dual Optimization. Entropy, 22.10.3390/e22020197 ↗
37. Choong, J.J., Liu, X., and Murata, T. (2018, January 17–20). Learning community structure with variational autoencoder. Proceedings of the IEEE International Conference on Data Mining, Singapore.10.1109/ICDM.2018.00022 ↗
38. Chen, Z., Chen, C., Zhang, Z., Zheng, Z., and Zou, Q. (2019, January 10–16). Variational graph embedding and clustering with Laplacian eigenmaps. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), Macao, China.10.24963/ijcai.2019/297 ↗
39. Parameters sharing in residual neural networks10.1007/s11063-019-10143-4 ↗
40. Multi-scale weight sharing network for image recognition10.1016/j.patrec.2020.01.011 ↗
41. Deep learning-based community detection in complex networks with network partitioning and reduction of trainable parameters10.1007/s12652-020-02389-x ↗
42. Cui, G., Zhou, J., Yang, C., and Liu, Z. (2020, January 23–27). Adaptive Graph Encoder for Attributed Graph Embedding. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Event, Online.10.1145/3394486.3403140 ↗
43. Finding community structure in very large networks10.1103/PhysRevE.70.066111 ↗
44. Fast unfolding of communities in large networks10.1088/1742-5468/2008/10/P10008 ↗
45. A new deep sparse autoencoder for community detection in complex networks10.1186/s13638-020-01706-4 ↗
46. Chen, Z., Li, X., and Bruna, J. (2019, January 6–9). Supervised Community Detection with Line Graph Neural Networks. Proceedings of the 7th International Conference on Learning Representations, New Orleans, LA, USA.
47. He, D., You, X., Feng, Z., Jin, D., Yang, X., and Zhang, W. (2018, January 2–7). A Network-Specific Markov Random Field Approach to Community Detection. Proceedings of the 32th AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.10.1609/aaai.v32i1.11281 ↗
48. Jin, D., Liu, Z., Li, W., He, D., and Zhang, W. (February, January 27). Graph Convolutional Networks Meet Markov Random Fields: Semi-Supervised Community Detection in Attribute Networks. Proceedings of the Thirty-Third Conference on Artificial Intelligence (AAAI-19), Honolulu, HI, USA.
49. He, D., Song, Y., Jin, D., Feng, Z., Zhang, B., Yu, Z., and Zhang, W. (2020, January 11–17). Community-Centric Graph Convolutional Network for Unsupervised Community Detection. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20), Yokohama, Japan.10.24963/ijcai.2020/486 ↗
50. Sperlí, G. (2019, January 8–12). A deep learning based community detection approach. Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, Limassol, Cyprus.10.1145/3297280.3297574 ↗
51. Edge classification based on Convolutional Neural Networks for community detection in complex network10.1016/j.physa.2020.124826 ↗
52. Deep convolutional neural networks for feature extraction of images generated from complex networks topologies10.1007/s11277-018-5445-7 ↗
53. Early prediction of scientific impact based on multi-bibliographic features and convolutional neural network10.1109/ACCESS.2019.2927011 ↗
54. MeshCNN: A network with an edge
55. Customized high performance and energy efficient communication networks for AI chips10.1109/ACCESS.2019.2916338 ↗
56. Wang, C., Pan, S., Hu, R., Long, G., Jiang, J., and Zhang, C. (2019, January 10–16). Attributed Graph Clustering: A Deep Attentional Embedding Approach. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), Macao, China.10.24963/ijcai.2019/509 ↗
57. Shchur, O., and Günnemann, S. (2019, January 4–8). Overlapping Community Detection with Graph Neural Networks. Proceedings of the First International Workshop on Deep Learning on Graphs (In Conjunction with the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining), Anchorage, AK, USA.
58. CayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters10.1109/TSP.2018.2879624 ↗
59. Convolutional neural networks on graphs with fast localized spectral filtering
60. Bo, D., Wang, X., Shi, C., Zhu, M., Lu, E., and Cui, P. (2020, January 20–24). Structural Deep Clustering Network. Proceedings of the Web Conference, Taipei, Taiwan.10.1145/3366423.3380214 ↗
61. Cheng, J., Wang, Q., Tao, Z., Xie, D., and Gao, Q. (2020, January 11–17). Multi-View Attribute Graph Convolution Networks for Clustering. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20), Yokohama, Japan.10.24963/ijcai.2020/411 ↗
62. Zhang, X., He, L., Chen, K., Luo, Y., Zhou, J., and Wang, F. (2018, January 3–7). Multi-view graph convolutional network and its applications on neuroimage analysis for parkinson’s disease. Proceedings of the AMIA Annual Symposium Proceedings, San Francisco, CA, USA.
63. Ma, T., Xiao, C., Zhou, J., and Wang, F. (2018, January 13–19). Drug similarity integration through attentive multiview graph auto-encoders. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18), Stockholm, Sweden.10.24963/ijcai.2018/483 ↗
64. Shi, M., Tang, Y., Zhu, X., Wilson, D., and Liu, J. (2020, January 11–17). Multi-Class Imbalanced Graph Convolutional Network Learning. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20), Yokohama, Japan.10.24963/ijcai.2020/398 ↗
65. Hu, R., Pan, S., Long, G., Lu, Q., Zhu, L., and Jiang, J. (2020, January 7–12). Going Deep: Graph Convolutional Ladder-Shape Networks. Proceedings of the Thirty-Fourth Conference on Artificial Intelligence (AAAI-20), New York, NY, USA.
66. Liu, Y., Wang, X., Wu, S., and Xiao, Z. (2020, January 7–12). Independence Promoted Graph Disentangled Networks. Proceedings of the Thirty-Fourth Conference on Artificial Intelligence (AAAI-20), New York, NY, USA.
67. Learning Graph Embedding With Adversarial Training Methods10.1109/TCYB.2019.2932096 ↗
68. Jia, Y., Zhang, Q., Zhang, W., and Wang, X. (2019, January 13). CommunityGAN: Community Detection with Generative Adversarial Nets. Proceedings of the WWW ’19: The World Wide Web Conference, San Francisco, CA, USA.10.1145/3308558.3313564 ↗
69. Zhang, Y., Xiong, Y., Ye, Y., Liu, T., Wang, W., Zhu, Y., and Yu, P.S. (2020, January 6–10). SEAL: Learning Heuristics for Community Detection with Generative Adversarial Networks. Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, Online.10.1145/3394486.3403154 ↗
70. Learning Graph Representation with Generative Adversarial Nets
71. Yang, L., Wang, Y., Gu, J., Wang, C., Cao, X., and Guo, Y. (2020, January 11–17). JANE: Jointly Adversarial Network Embedding. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20), Yokohama, Japan.10.24963/ijcai.2020/192 ↗
72. Kulkarni, S., Katariya, J.K., and Potika, K. (2020, January 7–10). GloVeNoR: GloVe for Node Representations with Second Order Random Walks. Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), The Hague, The Netherlands.10.1109/ASONAM49781.2020.9381347 ↗
73. Billings, J.C.W., Hu, M., Lerda, G., Medvedev, A.N., Mottes, F., Onicas, A., Santoro, A., and Petri, G. (2019). Simplex2Vec embeddings for community detection in simplicial complexes. arXiv.
74. Engineering parallel algorithms for community detection in massive networks10.1109/TPDS.2015.2390633 ↗