Community detection in complex networks using network embedding and gravitational search algorithm

The structural and functional characteristic features of nodes can be analyzed by visualizing community structure in complex networks. Community detection helps us to detect nodes having similar behavior in a system and organize the network into a network of closely connected groups or modules. Network embedding technique represents the nodes of the input graph into vector space and preserves their inherent and topological features and can contribute significantly to various applications in network analysis. In this paper, we propose a novel community detection method using network embedding technique. Firstly, nodes of the graph are embedded in feature space of d dimensions, and then low-rank approximation is applied to avoid the results from being affected by noise or outliers. Further, k-means clustering is employed to find the centroids of the clusters in the network and followed by a gravitational search algorithm to improve the results of centroids of clusters. Finally, we compute the effectiveness of detected communities using different performance measures. Our method serves as a universal framework towards applying and bench-marking various embedding techniques in graphs for performing community detection. We perform the test using various evaluation criteria on several real-life and synthetic networks and the obtained result reveals the utility of the proposed algorithm.