International Journal of Computer Networks and Applications

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Discovery of Multi-Objective Overlapping Communities within Social Networks Using a Socially Inspired Metaheuristic Algorithm


Affiliations
1 Department of Software Engineering, Firat University, Elazig, Turkey
2 Department of Software Engineering, Firat University, Elazig, India
 

Frequently studied structural property of networks is community structure which is described as a group of users. User interactions inside the group are more than those outside the group. Communities in networks may be overlapped as users belong to multiple groups at once. This paper proposes a new socially inspired metaheuristic search and optimization algorithm, Parliamentary Optimization Algorithm (POA), to acquire promising solutions to overlapping community detection problems considering multiple objectives. The salient and unique feature of this work is that for the first time POA has been designed as a multi-objective search method for overlapping community detection. There is not any work about multi-objective overlapping community detection problem in the related literature. For this reason, simulation results of the proposed algorithm have not been compared with any results of works. The experimental studies on both artificial and real world social networks indicate that the POA ensures beneficial results for defining multi-objective overlapping community structure. A novel and interesting application area of POA has been introduced with this work. Parallel and distributed versions of social based POA with optimized parameters may also be efficiently designed and used for different social network problems.

Keywords

Complex Networks, Computational Intelligence, Evolutionary Computation, Heuristic Algorithms.
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  • F. Altunbey, B. Alatas, “Overlapping community detection in social networks using parliamentary optimization algorithm”, International Journal of Computer Networks and Applications, vol. 2, 2015, pp. 12–19.

  • X. Zhou, Y. Liu, J. Zhang, T. Liu, D. Zhang, “An ant colony based algorithm for overlapping community detection in complex networks”, Physica A: Statistical Mechanics and its Applications, vol. 427, 2015, pp. 289–301.

  • ZH. Wu, YF. Lin, S. Gregory, HY. Wan, SF. Tian, “Balanced multi-label propagation for overlapping community detection in social networks”, Journal of Computer Science and Technology, vol. 27, 2012, pp. 468–479.

  • L. Huang, G. Wang, Y. Wang, E. Blanzieri, C. Su, “Link clustering with extended link similarity and EQ evaluation division”, Plos One, vol. 8, 2013.

  • W. Wang, D. Liu, X. Liu, L. Pan, “Fuzzy overlapping community detection based on local random walk and multidimensional scaling”. Physica A: Statistical Mechanics and its Applications, vol. 392, 2013, pp. 6578–6586.

  • L. Zhou, K. Lu, P. Yang, L. Wang, B. Kong, “An approach for overlapping and hierarchical community detection in social networks based on coalition formation game theory”, Expert Systems with Applications, vol. 42, 2015, pp. 9634–9646.

  • X. Qi, R. Luo, E. Fuller, R. Luo, C. Zhang, “Signed Quasi-Clique Merger: A new clustering method for signed networks with positive and negative edges”, International Journal of Pattern Recognition and Artificial Intelligence, vol. 30, 2016.

  • C. Tong, Z. Xie, X. Mo, J. Niu, Y. Zhang, “Detecting overlapping communities of weighted networks by central figure algorithm. In: Proceeding of Computing”, Communications and IT Applications (ComComAp), 2014, pp. 7–12.

  • AG. Nikolaev, R. Razib, A. Kucheriya, “On efficient use of entropy centrality for social network analysis and community detection”, Social Networks, vol. 40, 2015, pp. 154–162.

  • Q. Dai, M. Guo, Y. Liu, X. Liu, L. Chen, “MLPA: Detecting overlapping communities by multi-label propagation approach”. In: Proceeding of 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 681–688.

  • K. Zhou, A. Martin, Q. Pan, “A similarity-based community detection method with multiple prototype representation”, Physica A: Statistical Mechanics and its Applications, vol. 438, 2015, pp. 519–531.

  • Y. Li, Y. Wang, J. Chen, L. Jiao, R. Shang, “Overlapping community detection through an improved multi-objective quantum-behaved particle swarm optimization”. Journal of Heuristics, vol. 21, 2015.

  • Z. Lu, X. Sun, Y. Wen, G. Cao, TL. Porta, “Algorithms and applications for community detection in weighted networks”. IEEE Transactions on Parallel and Distributed Systems, vol. 26, 2015.

  • H. Zhang, X. Chen, J. Li, B. Zhou, “Fuzzy community detection via modularity guided membership-degree propagation”, Pattern Recognition Letters, vol. 70, 2015, pp. 66-72.

  • S. Kianian, MR. Khayyambashi, N. Movahhedinia, “Semantic community detection using label propagation algorithm”, Journal of Information Science, vol.42, 2016, pp. 166–178.

  • C. Zhang, X. Hei, D. Yang, L. Wang, “A memetic particle swarm optimization algorithm for community detection in complex networks”, International Journal of Pattern Recognition and Artificial Intelligence, vol. 30, 2016, 30.

  • M. Song, YK. Jeong, HJ. Kim, “Identifying the topology of the k-pop video community on YouTube: A combined co-comment analysis approach”, Journal of the Association for Information Science and Technology, vol. 66, 2015, pp. 2580–2595.

  • M. Hosseini-Pozveh, K. Zamanifar, AR. Naghsh-Nilchi, “A community-based approach to identify the most influential nodes in social networks”. Journal of Information Science, vol. 1, 2016, pp. 1–17.

  • M. Atzmueller, S. Doerfel, F. Mitzlaff, “Description-oriented community detection using exhaustive subgroup discovery”, Information Sciences, vol. 329, 2016, pp. 965–984.

  • A. Borji, M. Hamidi, “A new approach to global optimization motivated by parliamentary political competitions”, International Journal of Innovative Computing, Information and Control, vol.5, 2009, pp.1643–1653.

  • F. Altunbey, B. Alatas, “Review of social-based artificial intelligence optimization algorithms for social network analysis” International Journal of Pure and Applied Sciences, vol. 1, 2015.

  • A. Borji, “Heuristic function optimization inspired by social competitive behaviors”, Applied Sciences, vol. 8, 2008, pp. 2105–2111.

  • S. Kiziloluk, B. Alatas, “Automatic mining of numerical classification rules with parliamentary optimization algorithm”, Advances in Electrical and Computer Engineering, vol. 4, 2015, pp. 17–24.

  • H. Shen, X. Cheng, K. Cai, MB. Hu, “Detect overlapping and hierarchical community structure in networks”, Physica A: Statistical Mechanics and its Applications, vol. 388, 2009, pp. 1706–1712.

  • J. Leskovec, KJ. Lang, MW. Mahoney, “Empirical comparison of algorithms for network community detection”, In: Proceeding 19th International Conference on World Wide Web, 2010, pp. 631–640.

  • LD. Christopher, P. Smyth, UCI Network Data Repository, http://networkdata.ics.uci.edu. (2008, accessed April 2016).

  • WW. Zachary, “An information flow model for conflict and fission in small groups”, Journal of Anthropological Research, vol. 33, 1977, pp. 452–473.

  • M. Girvan, ME. Newman, “Community structure in social and biological networks”, In: Proceeding of National Academy of Sciences, vol.99, 2002, pp. 7821–7826.

  • D. Lusseau, K. Schneider, OJ. Boisseau, P. Haase, E. Slooten, SM. Dawson, “The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations”, Behavioral Ecology and Sociobiology, vol. 54, 2003, 396–405.

  • DE. Knuth, “The Stanford Graph Base: A platform for combinatorial computing”, Addison-Wesley Reading, Boston, 1993.

  • B. Dickinson, B. Valyou, W. Hu, “A genetic algorithm for identifying overlapping communities in social networks using an optimized search space”. Social Networking, vol. 2, 2013, pp. 193–201.

  • C. Pizzuti, “Community detection in social networks with genetic algorithms”, In: Proceeding of 10th Annual Conference on Genetic and Evolutionary Computation ACM, 2008, pp. 1137–1138.


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  • Discovery of Multi-Objective Overlapping Communities within Social Networks Using a Socially Inspired Metaheuristic Algorithm

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Authors

Feyza Altunbey Ozbay
Department of Software Engineering, Firat University, Elazig, Turkey
Bilal Alatas
Department of Software Engineering, Firat University, Elazig, India

Abstract


Frequently studied structural property of networks is community structure which is described as a group of users. User interactions inside the group are more than those outside the group. Communities in networks may be overlapped as users belong to multiple groups at once. This paper proposes a new socially inspired metaheuristic search and optimization algorithm, Parliamentary Optimization Algorithm (POA), to acquire promising solutions to overlapping community detection problems considering multiple objectives. The salient and unique feature of this work is that for the first time POA has been designed as a multi-objective search method for overlapping community detection. There is not any work about multi-objective overlapping community detection problem in the related literature. For this reason, simulation results of the proposed algorithm have not been compared with any results of works. The experimental studies on both artificial and real world social networks indicate that the POA ensures beneficial results for defining multi-objective overlapping community structure. A novel and interesting application area of POA has been introduced with this work. Parallel and distributed versions of social based POA with optimized parameters may also be efficiently designed and used for different social network problems.

Keywords


Complex Networks, Computational Intelligence, Evolutionary Computation, Heuristic Algorithms.

References