Enhancement of Existing Community Detection Method Using Similarity Based Link Prediction
DOI:
https://doi.org/10.18486/ijcsnt/4.3.058Abstract
Link prediction and community detection are two major area of link mining which separately studies over decades. Link prediction is used to predict the possible future links and hidden links in complex network while community detection deals with topological structure of the network to find new communities and change in the network structure over time. Community detection refers to the problem of finding such groups in real world social network. Link prediction and community detection both are based on relations between vertices in the network. Related work shows that the use of link prediction is beneficial for detecting new communities in complex network that has given very less attention. This paper is based on a different approach to community detection through link prediction that uses the Girvan Newman method.
References
Huang Z, Li X and Chen H. Link prediction approach to collaborative filtering. In Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries. NK, pp. 141–142. DOI: https://doi.org/10.1145/1065385.1065415
Airoldi EM. Mixed membership block models for relational data with application to protein-protein interactions. In Proceedings of International Biometric Society-ENAR Annual Meetings.
Hasan MA, Chaoji V, Salem S et al. Link prediction using supervised learning. In SDM Workshop of Link Analysis. San Francisco, Calif, USA.
Gupta P and Sharma S. A survey on link prediction problem in social network. International Journal for Science and Advance Research in Technology (IJSART) 2015; 1(1).
Girvan M and Newman MEJ. Community structures in social and biological networks 2002; 99(12): 7821–7826. DOI: https://doi.org/10.1073/pnas.122653799
Newman MEJ and Girvan M. Finding and evaluating community structure in networks. Physical Review E 2004; 69: 026113. DOI: https://doi.org/10.1103/PhysRevE.69.026113
Yan B and Gregory S. Detecting community structure in networks using edge prediction methods. CoRR 2012; abs/1201.3466. DOI: https://doi.org/10.1088/1742-5468/2012/09/P09008
Li F et al. A clustering-based link prediction method in social networks 2014; 29: 432–442. DOI: https://doi.org/10.1016/j.procs.2014.05.039
Liben-Nowell D and Kleinberg J. The link prediction problem for social networks. In Proceedings 12th International Conference on Information and Knowledge Management (CIKM’2003). DOI: https://doi.org/10.1145/956863.956972
Tylenda T, Angelova R and Bedathur S. Towards time-aware link prediction in evolving social networks. In Proceedings 3rd Workshop on Social Network Mining and Analysis (SNA-KDD’2009). pp. 1–10. DOI: https://doi.org/10.1145/1731011.1731020
Dong L et al. The algorithm of link prediction on social network. Mathematical Problems in Engineering, Hindawi Publishing Corporation 2013; Article ID 125123, 7 pages. DOI: https://doi.org/10.1155/2013/125123
Lu L and Zhou T. Role of weak ties in link prediction of complex networks. arXiv:09071728v2 [cs.IR] 2009. DOI: https://doi.org/10.1145/1651274.1651285
Zachary W. An information flow model for conflict and fission in small groups. Journal of Anthropological Research 1977; 33: 452–473. DOI: https://doi.org/10.1086/jar.33.4.3629752
Kossinets G. Effects of missing data in social networks. Social Networks 2006; 28(3): 247–268. DOI: https://doi.org/10.1016/j.socnet.2005.07.002
Jaccard P. Etude comparative de la distribution florale dans une portion des alpes et des jura. Bulletin de la Societe Vaudoise des Science Naturelles 1901; 37(547).
Adamic LA and Adar E. Friends and neighbours on the web. Social Networks 2003; 25(3): 211–230. DOI: https://doi.org/10.1016/S0378-8733(03)00009-1
Barabasi AL, Jeong H, Neda Z et al. Evolution of the social network of scientific collaboration. Physica A 2002; 311(3–4): 590–614. DOI: https://doi.org/10.1016/S0378-4371(02)00736-7
Katz L. A new status index derived from sociometric analysis. Psychometrika 1953; 18(1): 39–43. DOI: https://doi.org/10.1007/BF02289026
Anderson BS, Butts C and Carley K. The interaction of size and density with graph-level indices. Social Networks 1999; 21: 239–267. DOI: https://doi.org/10.1016/S0378-8733(99)00011-8
Liu Z, Zhang QM, Lü L et al. Link prediction in complex networks: A local naive Bayes model. EPL (Europhysics Letters) 2011; 96. DOI: https://doi.org/10.1209/0295-5075/96/48007
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Pallavi Gupta, Sanjiv Sharma
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
