Abstract
Community detection problem is a well-studied problem in social networks. A good community can be defined as a group of nodes that are highly connected with each other and loosely connected to the nodes outside the community. Regarding the fact that social networks are huge in size, having complete information of the whole network is almost impossible. As a result, the problem of local community detection has become more popular in recent years. In order to detect local communities, researchers mostly utilize an evaluation metric along with an algorithm to explore local communities. In this paper, the weaknesses of some well-known metrics are considered and a new metric to evaluate the quality of a community, only using local information, is proposed by using geodesic distance. The proposed metric can make a reasonable trade-off between the number of external edges and the density of the community. Furthermore, the experimental results of this study demonstrate that this metric could be useful in terms of evaluating the communities of real social networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chen, J., Zaïane, O., Goebel, R.: Local community identification in social networks. In: 2009 International Conference on Advances in Social Network Analysis and Mining, pp. 237–242. IEEE (2009)
Clauset, A.: Finding local community structure in networks. Phys. Rev. E 72(2), 026132 (2005)
Dao, V.-L., Bothorel, C., Lenca, P.: Estimating the similarity of community detection methods based on cluster size distribution. In: Aiello, L.M., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L.M. (eds.) COMPLEX NETWORKS 2018. SCI, vol. 812, pp. 183–194. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05411-3_15
Dourisboure, Y., Geraci, F., Pellegrini, M.: Extraction and classification of dense communities in the web. In: Proceedings of the 16th International Conference on World Wide Web, pp. 461–470 (2007)
Evans, T.S.: Clique graphs and overlapping communities. J. Stat. Mech.: Theory Exp. 2010(12), P12037 (2010)
Ghasemian, A., Hosseinmardi, H., Clauset, A.: Evaluating overfit and underfit in models of network community structure. IEEE Trans. Knowl. Data Eng. 32, 1722–1735 (2019)
Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)
Jebabli, M., Cherifi, H., Cherifi, C., Hamouda, A.: Community detection algorithm evaluation with ground-truth data. Physica A: Stat. Mech. Appl. 492, 651–706 (2018)
Lakhdari, A., Chorana, A., Cherroun, H., Rezgui, A.: A link strength based label propagation algorithm for community detection. In: 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom), pp. 362–369. IEEE (2016)
Luo, F., Wang, J.Z., Promislow, E.: Exploring local community structures in large networks. In: 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings), WI 2006, pp. 233–239. IEEE (2006)
Luo, W., Lu, N., Ni, L., Zhu, W., Ding, W.: Local community detection by the nearest nodes with greater centrality. Inf. Sci. 517, 377–392 (2020)
Luo, W., Zhang, D., Jiang, H., Ni, L., Hu, Y.: Local community detection with the dynamic membership function. IEEE Trans. Fuzzy Syst. 26(5), 3136–3150 (2018)
Luo, W., Zhang, D., Ni, L., Lu, N.: Multiscale local community detection in social networks. IEEE Trans. Knowl. Data Eng. 1–1 (2019). https://doi.org/10.1109/TKDE.2019.2938173
Lusseau, D., Schneider, K., Boisseau, O.J., Haase, P., Slooten, E., Dawson, S.M.: The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations. Behav. Ecol. Sociobiol. 54(4), 396–405 (2003). https://doi.org/10.1007/s00265-003-0651-y
Price, B.L., Morse, B., Cohen, S.: Geodesic graph cut for interactive image segmentation. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3161–3168. IEEE (2010)
Wu, L., Bai, T., Wang, Z., Wang, L., Hu, Y., Ji, J.: A new community detection algorithm based on distance centrality. In: 2013 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 898–902. IEEE (2013)
Xiao, Y., Siebert, P., Werghi, N.: Topological segmentation of discrete human body shapes in various postures based on geodesic distance. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 3, pp. 131–135. IEEE (2004)
Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33(4), 452–473 (1977)
Zhen-Qing, Y., Ke, Z., Song-Nian, H., Jun, Y.: A new definition of modularity for community detection in complex networks. Chin. Phys. Lett. 29(9), 098901 (2012)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Bakhtar, S., Gholami, M.S., Harutyunyan, H.A. (2020). A New Metric to Evaluate Communities in Social Networks Using Geodesic Distance. In: Chellappan, S., Choo, KK.R., Phan, N. (eds) Computational Data and Social Networks. CSoNet 2020. Lecture Notes in Computer Science(), vol 12575. Springer, Cham. https://doi.org/10.1007/978-3-030-66046-8_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-66046-8_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-66045-1
Online ISBN: 978-3-030-66046-8
eBook Packages: Computer ScienceComputer Science (R0)