Community Detection in Complex Networks: A Novel Approach Based on Ant Lion Optimizer

The problem of community detection in complex networks has established an increased amount of interest since the past decade. Community detection is a way to discover the structure of network by assembling the nodes into communities. The grouping performed for the communities encompasses denser interconnection between the nodes than community’s intra-connections. In this paper a novel nature-inspired algorithmic approach based on Ant Lion Optimizer for efficiently discovering the communities in large networks is proposed. The proposed algorithm optimizes modularity function and is able to recognize densely linked clusters of nodes having sparse interconnects. The work is tested on Zachary’s Karate Club, Bottlenose Dolphins, Books about US politics and American college football network benchmarks and results are compared with the Ant Colony Optimization (ACO) and Enhanced Firefly algorithm (EFF) approaches. The proposed approach outperforms EFF and ACO for Zachary and Books about US politics and produces results better than ACO for Dolphins and EFF for American Football Club. The conclusion drawn from experimental results illustrates the potential of the methodology to effectively identify the network structure.

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