A Hybrid Approach to Detect Researchers’ Communities Based on Deep Learning and Game Theory

Document Type : Original Article


1 Department of Computer, South Tehran Branch, Islamic Azad University, Tehran, Iran

2 E-Services and E-Content Research Group, IT Research Faculty, ICT Research Institute, Tehran, Iran

3 Department of Information Technology, ICT Research Institute, Tehran, Iran

4 Sharif University of Technology, Tehran, Iran


Today, with the proliferation of complex networks and their large amounts of data, researchers have great concerns about the accurate community detection methods. The difficulty in analyzing these networks stems from their enormous size and the complex relationships among the members of the networks. It is difficult to analyze the deep relationships and mechanisms by just looking at the whole. Traditional methods have some problems and limitations when analyzing these networks such as feature extraction, high reliance on the initial phase settings, computational complexity, neglect of network relationships and content. From the perspective of relationships and interactions between individuals, the environment of complex networks can be compared to a game in which nodes acting as players or agents may join or leave a community based on similar structural or semantic characteristics. Consequently, there is a strong tendency to use cooperative and non-cooperative games to detect communities. Moreover, the amalgamation of deep learning techniques and game theory has recently been proven to be highly effective in extracting communities. Deep learning techniques have demonstrated enhanced capability in feature engineering and automate the process. In this study, the authors make effort to detect rational and accurate communities based on structural and content features with the help of traditional approaches, deep learning, as well as cooperative and non-cooperative games. The efficiency of this study is demonstrated by experimental findings on real datasets, and confirming that it is able enough to identify those communities that are more meaningful.


Main Subjects

  1. Fortunato, S., "Community detection in graphs", Physics Reports, Vol. 486, No. 3-5, (2010), 75-174. https://doi.org/10.1016/j.physrep.2009.11.002
  2. Coscia, M., Giannotti, F. and Pedreschi, D., "A classification for community discovery methods in complex networks", Statistical Analysis and Data Mining: The ASA Data Science Journal, Vol. 4, No. 5, (2011), 512-546. https://doi.org/10.1002/sam.10133
  3. Krishna Prasad, M. and Rao Chintalapudi, S., "Mining overlapping communities in real-world networks based on extended modularity gain", International Journal of Engineering, Transactions A: Basics, Vol. 30, No. 4, (2017), 486-492. doi: 10.5829/idosi.ije.2017.30.04a.05.
  4. Jonnalagadda, A. and Kuppusamy, L., "A survey on game theoretic models for community detection in social networks", Social Network Analysis and Mining, Vol. 6, (2016), 1-24. doi: 10.1007/s13278-016-0386-1.
  5. Nash, J., "Non-cooperative games. The annals of mathematics, second series, volume 54, issue 2", (1951).
  6. Zlotkin, G. and Rosenschein, J.S., "Coalition, cryptography, and stability: Mechanisms for coalition formation in task oriented domains, Alfred P. Sloan School of Management, Massachusetts Institute of Technology, (1994).
  7. Fadaei, M., Tavakkoli-Moghaddam, R., Taleizadeh, A. and Mohammaditabar, D., "Cooperative benefit and cost games under fairness concerns", International Journal of Engineering, Transactions B: Applications, Vol. 31, No. 8, (2018), 1250-1257. doi: 10.5829/ije.2018.31.08b.09.
  8. Perozzi, B., Al-Rfou, R. and Skiena, S., "Deepwalk: Online learning of social representations", in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. (2014), 701-710.
  9. Taheri, A., Gimpel, K. and Berger-Wolf, T., "Sequence-to-sequence modeling for graph representation learning", Applied Network Science, Vol. 4, (2019), 1-26. https://doi.org/10.1007/s41109-019-0174-8
  10. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S. and Dean, J., "Distributed representations of words and phrases and their compositionality", Advances in Neural Information Processing Systems, Vol. 26, No., (2013). doi: 10.48550/arXiv.1310.4546.
  11. Salehi, S. and Pouyan, A., "Detecting overlapping communities in social networks using deep learning", International Journal of Engineering, Transactions C: Aspects, Vol. 33, No. 3, (2020), 366-376. doi: 10.5829/ije.2020.33.03c.01.
  12. Indrawan, G., Setiawan, H. and Gunadi, A., "Multi-class svm classification comparison for health service satisfaction survey data in bahasa", HighTech and Innovation Journal, Vol. 3, No. 4, (2022), 425-442. doi: 10.28991/HIJ-2022-03-04-05.
  13. Chakraborty, T., Dalmia, A., Mukherjee, A. and Ganguly, N., "Metrics for community analysis: A survey", ACM Computing Surveys (CSUR), Vol. 50, No. 4, (2017), 1-37. doi: 10.1145/3091106.
  14. Cavallari, S., Zheng, V.W., Cai, H., Chang, K.C.-C. and Cambria, E., "Learning community embedding with community detection and node embedding on graphs", in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. (2017), 377-386.
  15. Van Laarhoven, T. and Marchiori, E., "Local network community detection with continuous optimization of conductance and weighted kernel k-means", The Journal of Machine Learning Research, Vol. 17, No. 1, (2016), 5148-5175.
  16. Liu, J., "Comparative analysis for k-means algorithms in network community detection", in Advances in Computation and Intelligence: 5th International Symposium, ISICA 2010, Wuhan, China, October 22-24, 2010. Proceedings 5, Springer. (2010), 158-169.
  17. Ferreira, L.N., Pinto, A. and Zhao, L., "Qk-means: A clustering technique based on community detection and k-means for deployment of cluster head nodes", in The 2012 International Joint Conference on Neural Networks (IJCNN), IEEE. (2012), 1-7.
  18. Niu, Y., Kong, D., Liu, L., Wen, R. and Xiao, J., "Overlapping community detection with adaptive density peaks clustering and iterative partition strategy", Expert Systems with Applications, Vol. 213, (2023), 119213. https://doi.org/10.1016/j.eswa.2022.119213
  19. Wu, Y., Fu, Y., Xu, J., Yin, H., Zhou, Q. and Liu, D., "Heterogeneous question answering community detection based on graph neural network", Information Sciences, Vol. 621, (2023), 652-671. https://doi.org/10.1016/j.ins.2022.10.126
  20. Chen, J., Li, Y., Yang, X., Zhao, S. and Zhang, Y., "Vghc: A variable granularity hierarchical clustering for community detection", Granular Computing, Vol. 6, (2021), 37-46. https://doi.org/10.1007/s41066-019-00195-1
  21. Yin, C., Zhu, S., Chen, H., Zhang, B. and David, B., "A method for community detection of complex networks based on hierarchical clustering", International Journal of Distributed Sensor Networks, Vol. 11, No. 6, (2015), 849140. https://doi.org/10.1155/2015/849140
  22. Zhou, L., Lü, K., Yang, P., Wang, L. and Kong, B., "An approach for overlapping and hierarchical community detection in social networks based on coalition formation game theory", Expert Systems with Applications, Vol. 42, No. 24, (2015), 9634-9646. https://doi.org/10.1016/j.eswa.2015.07.023
  23. Li, C., Bai, J., Wenjun, Z. and Xihao, Y., "Community detection using hierarchical clustering based on edge-weighted similarity in cloud environment", Information Processing & Management, Vol. 56, No. 1, (2019), 91-109. https://doi.org/10.1016/j.ipm.2018.10.004
  24. Newman, M.E., "Modularity and community structure in networks", Proceedings of the National Academy of Sciences, Vol. 103, No. 23, (2006), 8577-8582. https://doi.org/10.1073/pnas.0601602103
  25. Grover, A. and Leskovec, J., "Node2vec: Scalable feature learning for networks", in Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. (2016), 855-864.
  26. Wang, D., Cui, P. and Zhu, W., "Structural deep network embedding", in Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. (2016), 1225-1234.
  27. Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J. and Mei, Q., "Line: Large-scale information network embedding", in Proceedings of the 24th international conference on world wide web. (2015), 1067-1077.
  28. Yang, C., Liu, Z., Zhao, D., Sun, M. and Chang, E.Y., "Network representation learning with rich text information", in IJCAI. Vol. 2015, (2015), 2111-2117.
  29. Pan, S., Wu, J., Zhu, X., Zhang, C. and Wang, Y., "Tri-party deep network representation", Network, Vol. 11, No. 9, (2016), 12.
  30. Chang, J. and Blei, D., "Relational topic models for document networks", in Artificial intelligence and statistics, PMLR. (2009), 81-88.
  31. McSweeney, P.J., Mehrotra, K. and Oh, J.C., "A game theoretic framework for community detection", in 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, IEEE. (2012), 227-234.
  32. Zhou, L., Lü, K., Cheng, C. and Chen, H., "A game theory based approach for community detection in social networks", in Big Data: 29th British National Conference on Databases, BNCOD 2013, Oxford, UK, July 8-10, 2013. Proceedings 29, Springer. (2013), 268-281.
  33. Hajibagheri, A., Alvari, H., Hamzeh, A. and Hashemi, S., "Social networks community detection using the shapley value", in The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012), IEEE. (2012), 222-227.
  34. Avrachenkov, K.E., Kondratev, A.Y., Mazalov, V.V. and Rubanov, D.G., "Network partitioning algorithms as cooperative games", Computational Social Networks, Vol. 5, No. 1, (2018), 1-28. doi: 10.1186/s40649-018-0059-5.
  35. Zhou, X., Cheng, S. and Liu, Y., "A cooperative game theory-based algorithm for overlapping community detection", IEEE Access, Vol. 8, (2020), 68417-68425. doi: 10.1109/ACCESS.2020.2985397.
  36. Chen, W., Liu, Z., Sun, X. and Wang, Y., "A game-theoretic framework to identify overlapping communities in social networks", Data Mining and Knowledge Discovery, Vol. 21, (2010), 224-240. https://doi.org/10.1007/s10618-010-0186-6
  37. Narayanam, R. and Narahari, Y., "A game theory inspired, decentralized, local information based algorithm for community detection in social graphs", in Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), IEEE. (2012), 1072-1075.
  38. Alvari, H., Hashemi, S. and Hamzeh, A., "Detecting overlapping communities in social networks by game theory and structural equivalence concept", in Artificial Intelligence and Computational Intelligence: Third International Conference, AICI 2011, Taiyuan, China, September 24-25, 2011, Proceedings, Part II 3, Springer. (2011), 620-630.
  39. Havvaei, E. and Deo, N., "A game-theoretic approach for detection of overlapping communities in dynamic complex networks", International Journal of Computational Methods, Vol. 1, (2016), 313-324. https: //doi.org/10.48550/arXiv.1603.00509
  40. Zhao, X., Wu, Y., Yan, C. and Huang, Y., "An algorithm based on game theory for detecting overlapping communities in social networks", in 2016 International Conference on Advanced Cloud and Big Data (CBD), IEEE. (2016), 150-157.
  41. Moscato, V., Picariello, A. and Sperli, G., "Community detection based on game theory", Engineering Applications of Artificial Intelligence, Vol. 85, (2019), 773-782. https://doi.org/10.1016/j.engappai.2019.08.003
  42. Wang, Y., Bu, Z., Yang, H., Li, H.-J. and Cao, J., "An effective and scalable overlapping community detection approach: Integrating social identity model and game theory", Applied Mathematics and Computation, Vol. 390, (2021), 125601. https://doi.org/10.1016/j.amc.2020.125601
  43. Zhou, L., Yang, P., Lü, K., Wang, L. and Chen, H., "A fast approach for detecting overlapping communities in social networks based on game theory", in Data Science: 30th British International Conference on Databases, BICOD 2015, Edinburgh, UK, July 6-8, 2015, Proceedings 30, Springer. (2015), 62-73.
  44. Torkaman, A., Badie, K., Salajegheh, A., Bokaei, M.H. and Ardestani, S.F.F., "A four-stage algorithm for community detection based on label propagation and game theory in social networks", AI, Vol. 4, No. 1, (2023), 255-269. https://doi.org/10.3390/ai4010011
  45. Torkaman, A., Badie, K., Salajegheh, A., Bokaei, M.H. and Fatemi, S.F., "A hybrid deep network representation model for detecting researchers’ communities", Journal of AI and Data Mining, Vol. 10, No. 2, (2022), 233-243. https://doi.org/10.22044/jadm.2022.11243.2277
  46. Myerson, R., "Game theory: Analysis of conflict harvard univ", Press, Cambridge, Vol. 3, (1991). https://doi.org/10.2307/j.ctvjsf522
  47. MacQueen, J., "Some methods for classification and analysis of multivariate observations", in Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Oakland, CA, USA. Vol. 1, (1967), 281-297.
  48. Bholowalia, P. and Kumar, A., "Ebk-means: A clustering technique based on elbow method and k-means in wsn", International Journal of Computer Applications, Vol. 105, No. 9, (2014). doi: 10.5120/18405-9674.
  49. Newman, M.E. and Girvan, M., Mixing patterns and community structure in networks, in Statistical mechanics of complex networks. 2003, Springer.66-87.
  50. Lusseau, D., "The emergent properties of a dolphin social network", Proceedings of the Royal Society of London. Series B: Biological Sciences, Vol. 270, No. suppl_2, (2003), S186-S188. doi: 10.1098/rsbl.2003.0057.
  51. Giles, C.L., Bollacker, K.D. and Lawrence, S., "Citeseer: An automatic citation indexing system", in Proceedings of the third ACM conference on Digital libraries. (1998), 89-98.
  52. Ng, A., Jordan, M. and Weiss, Y., "On spectral clustering: Analysis and an algorithm", Advances in Neural Information Processing Systems, Vol. 14, (2001).
  53. Blondel, V.D., Guillaume, J.-L., Lambiotte, R. and Lefebvre, E., "Fast unfolding of communities in large networks", Journal of Statistical Mechanics: Theory and Experiment, Vol. 2008, No. 10, (2008), P10008. doi: 10.1088/1742-5468/2008/10/P10008.
  54. Pan, S., Hu, R., Long, G., Jiang, J., Yao, L. and Zhang, C., "Adversarially regularized graph autoencoder for graph embedding", arXiv preprint arXiv:1802.04407, (2018). https://doi.org/10.48550/arXiv.1802.04407
  55. Wang, C., Pan, S., Hu, R., Long, G., Jiang, J. and Zhang, C., "Attributed graph clustering: A deep attentional embedding approach", arXiv preprint arXiv:1906.06532, (2019). https://doi.org/10.48550/arXiv.1802.04407
  56. Liu, L., Kang, Z., Ruan, J. and He, X., "Multilayer graph contrastive clustering network", Information Sciences, Vol. 613, (2022), 256-267. https://doi.org/10.1016/j.ins.2022.09.042
  57. Qin, M., Jin, D., He, D., Gabrys, B. and Musial, K., "Adaptive community detection incorporating topology and content in social networks", in Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. (2017), 675-682.
  58. Kingma, D.P. and Ba, J., "Adam: A method for stochastic optimization", arXiv preprint arXiv:1412.6980, (2014). dhttps://doi.org/10.48550/arXiv.1412.6980
  59. Yang, H., Kong, Q. and Mao, W., "A deep latent space model for graph representation learning", arXiv preprint arXiv:2106.11721, (2021). https://doi.org/10.48550/arXiv.2106.11721
  60. Tatarkanov, A., Alexandrov, I., Muranov, A. and Lampezhev, A., "Development of a technique for the spectral description of curves of complex shape for problems of object classification", Emerging Science Journal, Vol. 6, No. 6, (2022), 1455-1475. doi: 10.28991/ESJ-2022-06-06-015.