Abstract
Considering the problems of communication overhead between nodes and the challenges brought by large-scale complex network in distributed system cluster management, we propose a local community detection algorithm in complex network called FLCDA (Fast Local Community Detection Algorithm). FLCDA can detect communities in a large-scale complex network on a single PC. The algorithm uses a Parallel Sliding Windows (PSW) method to break a large-scale network into smaller sub-networks, and load sub-network into a PC memory. This method conforms to the local characteristics of the community. FLCDA first finds out disjoint Maximum Influence k-clique in each sub-network, and then assigns the same label and weight to all nodes in the same Maximum Influence k-clique. These labels and weights are used as seeds at the label propagation phase of FLCDA. During the labeling propagation phase, FLCDA applies a synchronous update strategy while removes meaningless labels after each iteration. When all node labels are updated, the update process will be ended. This method can reduce the calculation cost and improve the stability. The experiment results show that non-parameter FLCDA can self-adaptively detect communities on various scales and types of complex networks. By comparing with other algorithms, FLCDA has gained higher community detection accuracy and less running time. Therefore, FLCDA algorithm can be better adapted to community detection in real complex networks without prior knowledge.
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References
Akbas, E., Zhao, P.: Truss-based community search: a truss-equivalence based indexing approach. Proc. VLDB Endowment 10(11), 1298–1309 (2017)
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 30(2), 155–168 (2008)
Chang, L.: Efficient maximum clique computation and enumeration over large sparse graphs. VLDB J. 29, 999–1022 (2020)
Dean, J., Ghemawat, S., MapReduce: simplified data processing on large clusters. In: Proceedings of the 6th conference on Symposium on Operating Systems Design & Implementation (OSDI), pp.137–150 (2004)
Deng, Z., Qiao, H., Song, Q., Gao, L.: A complex network community detection algorithm based on label propagation and fuzzy c-means. Phys. A 519, 217–226 (2019)
Dharwadker, A.: The Clique Algorithm. CreateSpace Independent Publishing Platform 1–46 (2011)
Ding, X., Zhang, J., Yang, J.: Node-community membership diversifies community structures: an overlapping community detection algorithm based on local expansion and boundary re-checking. Knowl.-Based Syst. 198, 105935 (2020)
Epasto, A., Lattanzi, S., Leme, R. P.: Ego-splitting framework: from non-overlapping to overlapping clusters. In: ACM SIGKDD, pp.145–154(2017)
Epasto, A., Lattanzi, S., Mirrokni, V., Sebe, I.O., Taei, A., Verma, S.: Ego-net community mining applied to friend suggestion. Proc. VLDB Endowment 9(4), 324–335 (2016)
Gregory, S.: Finding overlapping communities in networks by label propagation. New J. Phys. 12(10), 103018 (2010)
He, C., Tang, Y., Yang, A., Zhao, G., Liu, H., Huang, C.: Large-scale topic community mining based on distributed nonnegative matrix factorization. Sci. Sin. Informationis 46(6), 714–728 (2016)
Janez, K., Dušanka, J.: An improved branch and bound algorithm for the maximum clique problem. MATCH Commun. Math. Comput. Chem 58(3), 569–590 (2007)
Jia, H., Ratnavelu, K.: A semi-synchronous label propagation algorithm with constraints for community detection in complex networks. Sci. Rep. 7, 45836 (2017)
Jin, D., Zhang, B., Song, Y., He, D., Feng, Z., Chen, S., Li, W., Musial, K.: Modmrf: a modularity-based markov random field method for community detection. Neurocomputing 405, 218–228 (2020)
Jokar, E., Mosle, M.: Community detection in social networks based on improved label propagation algorithm and balanced link density. Phys. Lett. A 83(8), 718–727 (2019)
Kai, G., Li, K.: A new k-shell decomposition method for identifying influential spreaders of epidemics on community networks. J. Syst. Sci. Inf. 6(4), 366–375 (2018)
Kuang, Z., Martin, A., Quan, P., Liu, Z.: SELP: Semi-supervised evidential label propagation algorithm for graph data clustering. Int. J. Approx. Reason. 92, 139–154 (2018)
Kuzmin, K., Shah, S.Y., Szymanski, B.K.: Parallel overlapping community detection with SLPA. In: IEEE International Conference on Social Computing, pp.204–212 (2013)
Kyrola, A., Blelloch, G., Guestrin, C.: GraphChi: large-scale graph computation on just a PC. In: Usenix Conference on Operating Systems Design and Implementation, pp.31–46 (2014)
Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78(4), 046110 (2008)
Lee, C., Reid, F., Mcdaid, A., Hurley, N.: Detecting highly overlapping community structure by greedy clique expansion. eprint arXiv:1002.1827 pp.33–42 (2010)
Leskovec, J., Lang, K.J., Dasgupta, A., Mahoney, M.W.: Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters. Internet Math. 6(1), 29–123 (2009)
Li, C.: Brief analysis of runtime system oriented graph computation. Commun CCF 14(7), 23–28 (2018)
Li, C., Tang, Y., Tang, Z., Huang, Y., Yuan, C., Zhao, J.: Community detection model in large-scale academic social networks. J. Comput. Appl. 35(9), 2565–2568 (2015). (2573)
Li, C., Tang, Z., Tang, Y., Zhao, J., Huang, Y.: Community detection algorithm with local-first approach in social networks. J. Front. Comput. Sci. Technol. 12(8), 1263–1277 (2018)
Li, C.Y., Tang, Y., Lin, H., Yuan, C.Z., Mai, H.Q.: Parallel overlapping community detection algorithm in complex networks based on label propagation. Sci. Sin. Informationis 46(2), 212–227 (2016)
Li, J., Wang, X., Wu, P.: Review on community detection methods based on local optimization. Bull. Chin. Acad. Sci. 2, 238–247 (2015)
Li, P., Huang, L., Wang, C., Lai, J.: Community detection by motif-aware label propagation. ACM Trans. Knowl. Discov. Data 14(2), 1–19 (2019)
Li, Y., Sha, C., Huang, Y., Zhang, Y.: Community detection in attributed graphs: An embedding approach. AAAI 32(1), 338–345 (2018)
Li, Y., Wang, G., Zhao, Y., Zhu, F., Wu, Y.: Towards k-node connected component discovery from large networks. World Wide Web 23(2), 799–830 (2020)
Liu, W., Jiang, X., Pellegrini, M., Wang, X.: Discovering communities in complex networks by edge label propagation. Sci. Rep. 6, 22470 (2016)
Low, Y., Bickson, D., Gonzalez, J., Guestrin, C., Kyrola, A., Hellerstein, J.: Distributed GraphLab: a framework for machine learning data mining in the cloud. Proc. VLDB Endowment 5(8), 716–727 (2012)
Lu, H., Halappanavar, M., Kalyanaraman, A.: Parallel heuristics for scalable community detection. Parallel Comput. 47, 19–37 (2014)
Luo, L., Liu, Y., Qian, D.: Survey on in-memory computing technology. J. Softw. 27(8), 2147–2167 (2016)
McDaid, A.F., Greene, D., Hurley, N.: Normalized mutual information to evaluate overlapping community finding algorithms. CoRR abs/1110.2515 (2011)
Meng, J., Fu, D., Yang, T.: Semi-supervised soft label propagation based on mass function for community detection. In: 21st International Conference on Information Fusion (FUSION), pp. 1163–1170 (2018)
Michele, C., Giulio, R., Fosca, G., Dino, P.: Uncovering hierarchical and overlapping communities with a local-first approach. ACM Trans. Knowl. Discov. Data (TKDD) 9(1), 1–27 (2014)
Nicosia, V., Mangioni, G., Carchiolo, V., Mangioni, G.: Extending the definition of modularity to directed graphs with overlapping communities. J. Stat. Mech: Theory Exp. 2009(3), 3166–3168 (2009)
Ovelgönne, M.: Distributed community detection in web-scale networks. In: ASONAM, pp. 66–73 (2013)
Palla, G., Deranyi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7046), 814–818 (2005)
Pattabiraman, B., Patwary, M., Gebremedhin, A., Liao, W., Choudhary, A.: Fast Algorithms for the Maximum Clique Problem on Massive Sparse Graphs. Optimization Methods and Software, pp.1–14 (2012)
Qiao, S., Guo, J., Han, N., Zhang, X., Yuan, C., Tang, C.: Parallel algorithm for discovering communities in large-scale complex networks. Chin. J. Comput. 40(3), 687–700 (2017)
Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76(3), 036106 (2007)
Rigi, M., Moser, I., Farhangi, M., Lui, C.: Finding and tracking local communities by approximating derivatives in networks. World Wide Web 23, 1519–1551 (2020)
Shang, R., Zhang, W., Jiao, L.: Circularly searching core nodes based label propagation algorithm for community detection. Int. J. Pattern Recog. Artif. Intell. 30(08), 1659024.1-1659024.22 (2016)
Staudt, C.L., Meyerhenke, H.: Engineering parallel algorithms for community detection in massive networks. IEEE Trans. Parallel Distrib. Syst. 27(1), 171–184 (2016)
Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440–442 (1998)
Wla, B., Nla, B., Li, N., Wza, B., Wd, C.: Local community detection by the nearest nodes with greater centrality. Inf. Sci. 517, 377–392 (2020)
Xu, G., Zhang, Y., Li, L.: Web Mining and Social Networking. Springer US (2011)
Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth. Knowl. Inf. Syst. 42(1), 181–213 (2015)
Yi, X., Chen, H., Lan, J.: Multi-objective community detection algorithms based on correlation of evaluation indices. J. Chin. Comput. Syst. 41(9), 1806–1811 (2020)
Yin, H., Benson, A.R., Leskovec, J., Gleich, D.F.: Local higher-order graph clustering. In: ACM SIGKDD International Conference, pp. 555–564 (2017)
Yu, Z., Chen, J., Guo, K., Chen, Y.: Overlapping community detection based on influence and seeds extension. Chin. J. Electron. 47(01), 155–162 (2019)
Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, L.: Spark: cluster computing with working sets. In: Proceedings of the 2nd USENIX conference on Hot topics in cloud computing, pp.1–10 (2010)
Zhan, Q., Zhang, J., Yu, P., Xie, J.: Community detection for emerging social networks. World Wide Web 20(6), 1409–1441 (2017)
Zhang, Y., Liu, Y., Li, Q., Jin, R.: LILPA: A label importance based label propagation algorithm for community detection with application to core drug discovery. Neurocomputing 413(9), 107–133 (2020)
Zhang, Y., Xu Yu, J., Hou, J.: Web communities: analysis and construction. Springer Science & Business Media (2006)
Zhang, Y., Wang, J., Wang, Y., Zhou, L.: Parallel community detection on large networks with propinquity dynamics. In: SIGKDD, pp.997–1006 (2009)
Acknowledgements
This research is partially supported by the National NSFC(61807009, U1811263, 61772211), the Key Laboratory of the Education Department of Guangdong Province (2019KSYS009), and the Special projects in key fields of Guangdong Department of Education (2020ZDZX1062).
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Tang, Z., Tang, Y., Li, C. et al. A fast local community detection algorithm in complex networks. World Wide Web 24, 1929–1955 (2021). https://doi.org/10.1007/s11280-021-00931-1
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DOI: https://doi.org/10.1007/s11280-021-00931-1