Discrete Bat Algorithm and Application in Community Detection

With the rapid development of social media, users have more options to form spontaneously reading or watch- ing communities, and detecting such communities has significance both in technological aspects and commercial aspects. Theoretically community detecting problem is NP-hard problem and thus people inclined to choose heuristic algorithms based on objective optimization. Among these algorithms Bat-inspired Algorithm (BA) was originally proposed to solve continuous objective optimization problems. In this paper, authors explore the role of Bat-inspired Algorithm on detecting community structure in networks. This paper firstly introduces the definition of community and Bat-inspired Algorithm, then gives Discrete Bat Algorithm (hereinafter referred to as D-BA), the detailed design of detecting a classic community structure in the Karate club network and the standard of evaluation. Finally analyzes and evaluates the result.

[1]  Xin-She Yang,et al.  Bat algorithm for multi-objective optimisation , 2011, Int. J. Bio Inspired Comput..

[2]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[3]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[4]  W. Zachary,et al.  An Information Flow Model for Conflict and Fission in Small Groups , 1977, Journal of Anthropological Research.

[5]  Zhang Shichao,et al.  Discovering network community based on multi-objective optimization , 2013 .

[6]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  Li Cheng,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010 .

[8]  Matthieu Latapy,et al.  Computing Communities in Large Networks Using Random Walks , 2004, J. Graph Algorithms Appl..

[9]  Liaquat Hossain,et al.  An efficient multiobjective evolutionary algorithm for community detection in social networks , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[10]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  Dino Pedreschi,et al.  A classification for community discovery methods in complex networks , 2011, Stat. Anal. Data Min..