Community Detection from Signed Social Networks Using a Multi-objective Evolutionary Algorithm

In this paper, we propose a method for detecting communities from signed social networks with both positive and negative weights by modeling the problem as a multi-objective problem. In the experiments, both real world and synthetic signed networks whose size ranges from 100 to 1200 nodes are used to validate the performance of the new algorithm. A comparison is also made between the new algorithm and an effective existing algorithm, namely FEC. The experimental results show that our algorithm obtains a good performance on both real world and synthetic data, and outperforms FEC clearly.

[1]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[2]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[3]  Yi Wang,et al.  A Genetic Algorithm for Detecting Communities in Large-Scale Complex Networks , 2010, Adv. Complex Syst..

[4]  Pablo Jensen,et al.  Analysis of community structure in networks of correlated data. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  Jing Liu,et al.  A comparative analysis of evolutionary and memetic algorithms for community detection from signed social networks , 2013, Soft Computing.

[6]  He Dong,et al.  Genetic Algorithm with Local Search for Community Detection in Large-scale Complex Networks , 2011 .

[7]  Jiming Liu,et al.  Community Mining from Signed Social Networks , 2007, IEEE Transactions on Knowledge and Data Engineering.

[8]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[9]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[10]  Alex Alves Freitas,et al.  A Survey of Evolutionary Algorithms for Clustering , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[11]  Jing Liu,et al.  A Multiobjective Evolutionary Algorithm Based on Similarity for Community Detection From Signed Social Networks , 2014, IEEE Transactions on Cybernetics.

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

[13]  Clara Pizzuti,et al.  A Multiobjective Genetic Algorithm to Find Communities in Complex Networks , 2012, IEEE Transactions on Evolutionary Computation.

[14]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  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.

[16]  Zhi-Hua Zhou,et al.  Examining spectral space of complex networks with positive and negative links , 2012, Int. J. Soc. Netw. Min..

[17]  Kay Chen Tan,et al.  An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization , 2007, IEEE Transactions on Evolutionary Computation.

[18]  Clara Pizzuti,et al.  GA-Net: A Genetic Algorithm for Community Detection in Social Networks , 2008, PPSN.

[19]  K. E. Read,et al.  Cultures of the Central Highlands, New Guinea , 1954, Southwestern Journal of Anthropology.

[20]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[21]  Haifeng Du,et al.  A genetic algorithm with local search strategy for improved detection of community structure , 2010 .

[22]  Haluk Bingol,et al.  Community Detection in Complex Networks Using Genetic Algorithms , 2006, 0711.0491.

[23]  Réka Albert,et al.  Near linear time algorithm to detect community structures in large-scale networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[24]  P. Doreian,et al.  A partitioning approach to structural balance , 1996 .

[25]  Leon Danon,et al.  Comparing community structure identification , 2005, cond-mat/0505245.