A New Multi-Agent Bat Approach for Detecting Community Structure in Social Networks
الموضوعات : Journal of Computer & RoboticsSaeed Alidoost 1 , Behrooz Masoumi 2
1 - Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2 - Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
الکلمات المفتاحية: Swarm intelligence, Multi-agent systems, Social networks, Community Detection, Bat algorithm, Modularity,
ملخص المقالة :
The complex networks are widely used to demonstrate effective systems in the fields of biology and sociology. One of the most significant kinds of complex networks is social networks. With the growing use of such networks in our daily habits, the discovery of the hidden social structures in these networks is extremely valuable because of the perception and exploitation of their secret knowledge. The community structure is a great topological property of social networks, and the process to detect this structure is a challenging problem. In this paper, a new approach is proposed to detect non-overlapping community structure. The approach is based on multi-agents and the bat algorithm. The objective is to optimize the amount of modularity, which is one of the primary criteria for determining the quality of the detected communities. The results of the experiments show the proposed approach performs better than existing methods in terms of modularity.
[1] Latora, V.; Nicosia, V.; Russo, G., Complex networks: principles,methods and applications (2017).
[2] Chen, G.; Wang, X.; Li, X., Fundamentals of complex networks: models, structures and dynamics, John Wiley & Sons (2014).
[3] Reihanian, A.; Feizi-Derakhshi, M.; Aghdasi, H., "Community detection in social networks with node attributes based on multi-objective biogeography based optimization", Engineering Applications of Artificial Intelligence, vol. 62, pp. 51-67 (2017).
[4] Silva, T. C.; Zhao, L., "Machine learning in complex networks", in Springer (2016).
[5] Ghaderi, S.; Abdollahpouri, A.; Moradi, P., "On the modularity improvement for community detection in overlapping social networks", in IEEE - 2016 8th International Symposium on Telecommunications (IST) (2016).
[6] Estrada, E., "Introduction to complex networks: structure and dynamics", in Evolutionary Equations with Applications in Natural Sciences, no. Springer, pp. 93-131 (2015).
[7] Huang, J.; Yang, B.; Jin, D.; Yang, Y., "Decentralized mining social network communities with agents", Mathematical and Computer Modelling - Elsevier, vol. 57, no. 11-12, pp. 2998-3008 (2013).
[8] Girvan, M.; Newman, M. E., "Community structure in social and biological networks", Proceedings of the national academy of sciences, vol. 99, no. 12, pp. 7821-7826 (2002).
[9] Cazabet, R.; Amblard, F., "Simulate to Detect: A Multi-agent System for Community Detection", in IEEE, Lyon, France (2011).
[10] Christian Blum, D. M., Swarm Intelligence: Introduction and Applications (Natural Computing Series), Springer (2008).
[11] Bozorg oáiciga, H. A., "Development and application of the bat algorithm for optimizing the operation of reservoir systems", Journal of Water Resources Planning and Management (2014).
[12] Colin, T., "A comparison of BA, GA, PSO, BP, and LM for training feed forward neural networks in e-learning context", International Journal of Intelligent Systems and Applications, pp. 23-29 (2012).
[13] Lancichinetti, A.; Fortunato, S.; Radicchi, F., "Benchmark graphs for testing community detection algorithms", Physical review E, vol. 78, no. 4, 046110 (2008).
[14] Newman, M. E.; Girvan, M., "Finding and evaluating community structure in networks", Physical review E, vol. 69, no. 2, 026113 (2004).
[15] Pizzuti, C., "GA-Net: A genetic algorithm for community detection in social networks", in International Conference on Parallel Problem Solving from Nature (2008).
[16] Pizzuti, C., "A multi-objective genetic algorithm for community detection in networks", in Tools with Artificial Intelligence 21st International Conference (2009).
[17] Li, Y.; Liu, J.; Liu, C., "A comparative analysis of evolutionary and memetic algorithms for community detection from signed social networks", vol. 18, no. 2, pp. 329-348 (2014).
[18] Li, Z.; Liu, J., "A multi-agent genetic algorithm for community detection in complex networks", Physica A: Statistical Mechanics and its Applications, vol. 449, pp. 336-347 (2016).
[19] Atay, Y.; Koc, I.; Babaoglu, I.; Kodaz, H., "Community Detection from Biological and Social Networks: A Comparative Analysis of Metaheuristic Algorithms", Applied Soft Computing (2016).
[20] Cazabet, R.; Amblard, F., "Simulate to detect: a multi-agent system forc ommunity detection", in IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, Lyon, France (2011).
[21] Hassan, E. A.; Hafez, A. I.; Hassanien, A. E.; Fahmy, A. A., "A Discrete Bat Algorithm for the Community Detection Problem", in International Conference on Hybrid Artificial Intelligence Systems. HAIS 2015. Lecture Notes in Computer Science, vol. 9121. Springer, Cham (2015).
[22] Yang, X. S. , "A new meta heuristic bat-inspired algorithm," Nature inspired cooperative strategies for optimization (NISCO 2010), vol. 284, pp. 65-74, 2010.
[23] Yang, X. S., "Meta-heuristic optimization with applications: Demonstration via bat algorithm", in Proceedings of 5th Bioinspired Optimization Methods and Their Applications (BIOMA2012), Bohinj, Slovenia (2012).
[24] Yang, X. S.; Gandomi, A. H., "Bat algorithm: A novel approach for global engineering optimization", in Engineering Computations (2012).
[25] Koffka, K.; Ashok, S., "A comparison of BA, GA, PSO, BP, and LM for training feed forward neural networks in e-learning context", International Journal of Intelligent Systems and Applications, vol. 7,
p. 23–29 (2012).
[26] Liu, J.; Jing, H.; Tang, Y.Y., "Multi-agent oriented constraint satisfaction", Artificial Intelligence, vol. 136, no. 1, pp. 101-144 (2002).
[27] Park, Y.; Song, M., "A genetic algorithm for clustering problems", in Proceedings of the Third Annual Conference on Genetic Programming, pp. 568–575 (1998).
[28] Wasi Ul Kabir, Md.; Sakib, N.; Mustafizur, S.; Shafiul, M., "A Novel Adaptive Bat Algorithm to Control Explorations and Exploitations for Continuous Optimization Problems", International Journal of Computer Applications (0975 – 8887), vol. 94, no. 13, pp. 15-20 (2014).
[29] Labatut, V., "Generalised measures for the evaluation of community detection methods", International Journal of Social Network Mining, vol. 2, no. 1, pp. 44-63 (2015).
[30] Danon, L.; Diaz-Guilera, A.; Duch, J.; Arenas, A., "Comparing community structure identification", Journal of Statistical Mechanics: Theory and Experiment, no. 9, pp. 219-228 (2005).