Abstract
The community detection is considered as one of the most important tools to discover useful information in large scale networks, which is difficult to obtain by simple observations. A lot of research work has been done in the past. In addition, the finding of the most influential users in the network is also a challenging task. The current state of the art algorithms in community detection demonstrated their effectiveness on a variety of networks, most of them, however, suffer from scalability issues and few of them are largely dependent on the network topology. To address this problem, we propose a dynamic community structure method, for the detection of a community in large scale networks. In our proposed method, the community structure of a network is improved by finding the most influential community nodes over time. The proposed method overcomes the deficiencies of prior similar community detection methods. The experimental results proved the efficiency of our method over three states of the art algorithms on both synthetic benchmark networks and real-world networks.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Amin, F., Ahmad, A., Choi, G.S.: Community detection and mining using complex networks tools in social internet of things. In: TENCON 2018 – 2018 IEEE Region 10 Conference, pp. 2086–2091 (2018)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99, 7821 (2002)
Ahajjam, S., El Haddad, M., Badir, H.: A new scalable leader-community detection approach for community detection in social networks. Soc. Netw. 54, 41–49 (2018)
Javadi, S.H.S., Gharani, P., Khadivi, S.: Detecting community structure in dynamic social networks using the concept of leadership. In: Amini, M.H., Boroojeni, K.G., Iyengar, S.S., Pardalos, P.M., Blaabjerg, F., Madni, A.M. (eds.) Sustainable Interdependent Networks: From Theory to Application, pp. 97–118. Springer, Cham (2018)
Jagadishwari, V., Umadevi, V.: NBCD: neighborhood based community detection in dynamic social networks. In: 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 586–590 (2018)
Amin, F., Abbasi, R., Rehman, A., Choi, G.S.: An advanced algorithm for higher network navigation in social internet of things using small-world networks. Sensors 19, 2007 (2019)
Amin, F., Ahmad, A., Sang Choi, G.: Towards trust and friendliness approaches in the social internet of things. Appl. Sci. 9, 166 (2019)
Fortunato, S.: Community detection in graphs. Phys. Rep. 486, 75–174 (2010)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004)
Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Comput. Simul. 2008, P10008 (2008)
Chin, J.H., Ratnavelu, K.: Detecting community structure by using a constrained label propagation algorithm. PLoS ONE 11, e0155320 (2016)
Brandes, U., Delling, D., Gaertler, M., Gorke, R., Hoefer, M., Nikoloski, Z., et al.: On modularity clustering. IEEE Trans. Knowl. Data Eng. 20, 172–188 (2008)
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70, 066111 (2004)
Javadi, S.H.S., Khadivi, S., Shiri, M.E., Xu, J.: An ant colony optimization method to detect communities in social networks. In: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), pp. 200–203 (2014)
Amin, F., Zubair, M.: Energy-efficient clustering scheme for multihop wireless sensor network (ECMS). In: 17th IEEE International Multi Topic Conference 2014, pp. 131–136 (2014)
Cai, M., Wang, W., Cui, Y., Stanley, H.E.: Multiplex network analysis of employee performance and employee social relationships. Physica A 490, 1–12 (2018)
Mucha, P.J., Richardson, T., Macon, K., Porter, M.A., Onnela, J.-P.: Community structure in time-dependent, multiscale, and multiplex networks. Science 328, 876–878 (2010)
Amin, F., Ahmad, A., Choi, G.: To study and analyse human behaviours on social networks. In: 2018 4th Annual International Conference on Network and Information Systems for Computers (ICNISC), pp. 233–236 (2018)
Tahir, N., Hassan, A., Asif, M., Ahmad, S.: MCD: Mutually Connected Community Detection using clustering coefficient approach in social networks. In: 2019 2nd International Conference on Communication, Computing and Digital systems (C-CODE), pp. 160–165 (2019)
Su, Y., Liu, C., Niu, Y., Cheng, F., Zhang, X.: A community structure enhancement-based community detection algorithm for complex networks. In: IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1–14 (2019)
Newman, M.: Finding community structure in networks using the eigenvectors of matrices. arXiv preprint physics/0605087
Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76, 036106 (2007)
Barber, M.J., Clark, J.W.: Detecting network communities by propagating labels under constraints (2009)
Lin, Z., Zheng, X., Xin, N., Chen, D.: CK-LPA: efficient community detection algorithm based on label propagation with community kernel. Physica A 416, 386–399 (2014)
Rhouma, D., Romdhane, L.B.: An efficient algorithm for community mining with overlap in social networks. Expert. Syst. Appl. 41, 4309–4321 (2014)
Xu, H., Hu, Y., Wang, Z., Ma, J., Xiao, W.: Core-based dynamic community detection in mobile social networks. Entropy 15, 5419–5438 (2013)
Hagberg, A., Swart, P., Chult, D.S.: Exploring Network Structure, Dynamics, and Function Using Network X. Los Alamos National Lab. (LANL), Los Alamos, NM, USA (2008)
Ruhnau, B.: Eigenvector-centrality — a node-centrality? Soc. Netw. 22, 357–365 (2000)
Etude, P.J.: comparative de la distribution florale dans une portion des Alpes et des Jura. Bull. Soc. Vaud. Sci. Nat. 37, 547 (1901)
Hafez, A.I., Hassanien, A.E., Fahmy, A.A.: Testing community detection algorithms: a closer look at datasets. In: Social Networking, pp. 85–99. Springer (2014)
Ostoic, J.: Compositional Equivalence with actor attributes: positional analysis of the Florentine Families network. arXiv preprint arXiv:1804.09427 (2018)
Santos, J.M., Embrechts, M.: On the use of the adjusted rand index as a metric for evaluating supervised classification. In: International Conference on Artificial Neural Networks, pp. 175–184 (2009)
Acknowledgments
This research was supported by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under Industrial Technology Innovation Program. No. 10063130, Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2019R1A2C1006159), and MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2019-2016-0-00313) supervised by the IITP (Institute for Information & communications Technology Promotion).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Amin, F., Choi, JG., Choi, G.S. (2020). Community Detection Based on Social Influence in Large Scale Networks. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Web, Artificial Intelligence and Network Applications. WAINA 2020. Advances in Intelligent Systems and Computing, vol 1150. Springer, Cham. https://doi.org/10.1007/978-3-030-44038-1_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-44038-1_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44037-4
Online ISBN: 978-3-030-44038-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)