A Cultural Algorithm Based on Artificial Bee Colony Optimization for Community Detection in Signed Social Networks

In this paper, we propose a cultural algorithm for detecting communities in signed networks based on artificial bee colony optimization, which is a process of dual inheritance from both a micro-evolutionary level and a macro-evolutionary level. We define the population space and belief space of the algorithm which are fit for community detection problem in signed social networks. To validate the performance of the algorithm we make experiments on two real-world networks and comparative experiments with three existing detecting algorithms on synthetic networks. The experimental results show that our algorithm gets a good performance on real-world networks and outperforms other algorithms on most of synthetic networks.

[1]  Aboul Ella Hassanien,et al.  Networks Community Detection Using Artificial Bee Colony Swarm Optimization , 2014, IBICA.

[2]  William R. Penuel,et al.  The ‘New’ Science of Networks and the Challenge of School Change , 2007 .

[3]  Fang Wu,et al.  Finding communities in linear time: a physics approach , 2003, ArXiv.

[4]  Pooya Moradian Zadeh,et al.  Community detection in social networks by cultural algorithm , 2015, 2015 International Conference on Collaboration Technologies and Systems (CTS).

[5]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Sahin Albayrak,et al.  Spectral Analysis of Signed Graphs for Clustering, Prediction and Visualization , 2010, SDM.

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

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

[9]  Jure Leskovec,et al.  Predicting positive and negative links in online social networks , 2010, WWW '10.

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

[11]  Andrej Mrvar,et al.  An Analysis of the Slovene Parliamentary Parties Network , 2003 .

[12]  Inderjit S. Dhillon,et al.  Weighted Graph Cuts without Eigenvectors A Multilevel Approach , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Christian Bauckhage,et al.  The slashdot zoo: mining a social network with negative edges , 2009, WWW.

[14]  Nelson Francisco Favilla Ebecken,et al.  Exploring complex networks in the plankton , 2016, IEEE Latin America Transactions.

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

[16]  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.

[17]  Michael I. Jordan,et al.  On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.

[18]  Nagarajan Natarajan,et al.  Prediction and clustering in signed networks: a local to global perspective , 2013, J. Mach. Learn. Res..

[19]  Ramanathan V. Guha,et al.  Propagation of trust and distrust , 2004, WWW '04.

[20]  Pooya Moradian Zadeh,et al.  A Multi-Population Cultural Algorithm for Community Detection in Social Networks , 2015, ANT/SEIT.