Bat-Cluster: A Bat Algorithm-based Automated Graph Clustering Approach

Defining the correct number of clusters is one of the most fundamental tasks in graph clustering. When it comes to large graphs, this task becomes more challenging because of the lack of prior information. This paper presents an approach to solve this problem based on the Bat Algorithm, one of the most promising swarm intelligence based algorithms. We chose to call our solution, “Bat-Cluster (BC).” This approach allows an automation of graph clustering based on a balance between global and local search processes. The simulation of four benchmark graphs of different sizes shows that our proposed algorithm is efficient and can provide higher precision and exceed some best-known values.

[1]  Sandra Paterlini,et al.  Differential evolution and particle swarm optimisation in partitional clustering , 2006, Comput. Stat. Data Anal..

[2]  S. P. Rajagopalan,et al.  Multi-Swarm Particle Swarm Optimization for Energy-Effective Clustering in Wireless Sensor Networks , 2017, Wirel. Pers. Commun..

[3]  Yonghong Chen,et al.  Social learning differential evolution , 2016, Inf. Sci..

[4]  Soundar R. T. Kumara,et al.  Clustering social networks using ant colony optimization , 2011, Operational Research.

[5]  Choochart Haruechaiyasak,et al.  Graph-Based Concept Clustering for Web Search Results , 2015 .

[6]  Selma Ayse Özel,et al.  A hybrid approach of differential evolution and artificial bee colony for feature selection , 2016, Expert Syst. Appl..

[7]  Jiujun Cheng,et al.  Ant colony optimization with clustering for solving the dynamic location routing problem , 2016, Appl. Math. Comput..

[8]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[9]  Ganapati Panda,et al.  A survey on nature inspired metaheuristic algorithms for partitional clustering , 2014, Swarm Evol. Comput..

[10]  Xiuzhen Zhang,et al.  Ant colony clustering with fitness perception and pheromone diffusion for community detection in complex networks , 2013 .

[11]  Maoguo Gong,et al.  Greedy discrete particle swarm optimization for large-scale social network clustering , 2015, Inf. Sci..

[12]  Anuradha Pillai,et al.  Clustering in Aggregated User Profiles Across Multiple Social Networks , 2017 .

[13]  El Mokhtar En-Naimi,et al.  Energy-Efficient Hybrid K-Means Algorithm for Clustered Wireless Sensor Networks , 2017 .

[14]  Abdelhadi Fennan,et al.  XEWGraph : A tool for visualization and analysis of hypergraphs for a competitive intelligence system , 2015, 2015 6th International Conference on Information Systems and Economic Intelligence (SIIE).

[15]  Anass El Haddadi Fouille multidimensionnelle sur les données textuelles visant à extraire les réseaux sociaux et sémantiques pour leur exploitation via la téléphonie mobile , 2011 .

[16]  Maoguo Gong,et al.  Discrete particle swarm optimization for identifying community structures in signed social networks , 2014, Neural Networks.

[17]  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).

[18]  Parham Moradi,et al.  Integration of graph clustering with ant colony optimization for feature selection , 2015, Knowl. Based Syst..

[19]  David Camacho Bio-inspired clustering: Basic features and future trends in the era of Big Data , 2015, 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF).

[20]  C. Vasanthanayaki,et al.  Particle Swarm Optimization-Based Clustering by Preventing Residual Nodes in Wireless Sensor Networks , 2015, IEEE Sensors Journal.

[21]  Bernard Dousset,et al.  “Forced” Force Directed Placement: a New Algorithm for Large Graph Visualization , 2017 .

[22]  Xu Zhou,et al.  An ant colony based algorithm for overlapping community detection in complex networks , 2015 .

[23]  Reinhard Lipowsky,et al.  Dynamic pattern evolution on scale-free networks. , 2005, Proceedings of the National Academy of Sciences of the United States of America.