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A Method to Decide the Number of Additional Edges and Their Locations to Integrate the Communities by Using Fitness Function
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 Title & Authors
A Method to Decide the Number of Additional Edges and Their Locations to Integrate the Communities by Using Fitness Function
Jun, Byung-Hyun; Lee, Sang-Hoon; Han, Chi-Geun;
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 Abstract
In this paper, we propose a method to decide the additional edges in order to integrate two communitites A,B(, is the size of the set). The proposed algorithm uses a fitness function that shows the property of a community and the fitness function is defined by the number of edges which exist in the community and connect two nodes, one is in the community and the other is out of the community. The community has a strong property when the function has a large value. The proposed algorithm is a kind of greedy method and when a node of B is merged to A, the minimum number of additional edges is decided to increase the fitness function value of A. After determining the number of additional edges, we define the community connectivity measures using the node centrality to determine the edges locations. The connections of the new edges are fixed to maximize the connectivity measure of the combined community. The procedure is applied for all nodes in B to integrate A and B. The effectiveness of the proposed algorithm is shown by solving the Zachary Karate Club network.
 Keywords
community integration;fitness function;node centrality;community detection;
 Language
Korean
 Cited by
 References
1.
W.W. Zachary, "An Information Flow Model for Conflict and Fission in Small Groups", J. of Anthropological Research, Vol. 33, pp.452-473, 1977.

2.
S. Fortunato, "Community Detection in Graphs", Physics Reports, Vol. 486, No. 3-5, pp.75-174, Feb. 2010. crossref(new window)

3.
A. Clauset, "Finding Local Community Structure in Networks", Phys. Rev. E 72, 026132, Aug. 2005. crossref(new window)

4.
F. Luo, J.Z. Wang, and E. Promislow, "Exploring Local Community Structures in Large Networks", Proceeding WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence pp.233-239, 2006.

5.
J. Bagrow, "Evaluating Local Community Methods in Networks", J. Stat. Mech. P05001, 2008.

6.
J. Chen, O. Zaiane, and R. Goebel, "Local Community Identification in Social Networks", Social Network Analysis and Mining, ASONAM, 2009.

7.
A. Lancichinetti, S. Fortunato, and J. Kertesz, "Detecting the Overlapping and Hierarchical Community Structure in Complex Networks", New Journal of Physics, Vol. 11, 033015, Mar. 2009. crossref(new window)

8.
H. Tong, B.A. Prakash, T. Eliassi-Rad, M. Faloutsos, and C. Faloutsos, "Gelling, and Melting, Large Graphs by Edge Manipulation", CIKM '12 Proceedings of the 21st ACM international conference on Information and knowledge management, pp.245-254, 2012.

9.
B.H. Jun, and C.G. Han, "A method to decide the number of additional edges to integrate the communities in social network by using modularity", Journal of The Korea Society of Computer and Information, Vol. 18, No. 7, pp.101-109, Jul. 2013. crossref(new window)

10.
B.H. Jun, and C.G. Han, "A study on a community integration algorithm using vertex betweenness centrality", Proceedings of the Korea Information Processing Society Conference, Vol. 19, No. 2, pp.323-325, 2012.

11.
L. Freeman, "Centrality in social networks conceptual clarification". Social Networks, Vol. 1, No. 3, pp.215-239, 1979.

12.
T. Zhou, L. Lu, and Y. Zhang, "Predicting missing links via local information". The European Physical Journal B, Vol. 71, No. 4, pp.623-630, Oct. 2009. crossref(new window)