Community detection in complex networks using stacked autoencoders and crow search algorithm

The presence of community structures in complex networks reveals meaningful insights about such networks and their constituent entities. Finding groups of related nodes based on mutual interests, common features, objectives, or interactions in a network is known as community detection. In this paper, we propose a novel Stacked Autoencoder-based deep learning approach augmented by the Crow Search Algorithm (CSA)-based k-means clustering algorithm to uncover community structure in complex networks. As per our approach, firstly, we generate a modularity matrix for the input graph. The modularity matrix is then passed through a series of stacked autoencoders to reduce the dimensionality of the matrix while preserving the topology of the network and improving the computational time of the proposed algorithm. The obtained matrix is then provided as an input to a modified k-means clustering algorithm augmented with the crow search optimization to detect the communities. We use Crow Search Algorithm-based optimization to generate the initial centroids for the k-means algorithm instead of generating them randomly. We perform extensive experimental analysis on several real-world and synthetic datasets and evaluate various performance metrics. We compare the results obtained by our algorithm with several traditional and contemporary community detection algorithms. The obtained results reveal that our proposed method achieves commendable results.