Community Detection in Social Networks: A Deep Learning Approach Using Autoencoders

Abstract

Abstract: This research aims to propose a more sophisticated clustering and community detection technique in complex social networks through the use of neural networks; autoencoder, in particular. In the past, methods for network analysis and community detection used several graph algorithms, but with the advancements in deep learning, autoencoders are used for learning node features. These node representations are learnt using a neural network-based autoencoder and then clustering algorithms like k-means, agglomerative and spectral clustering are performed. These algorithms are then improved by incorporating with the Louvain algorithm for community detection. The proposed method, named the Spectral Louvain Algorithm, offers several advantages: it saves the stage of feature extraction, is suitable for Call Detail Record (CDR) and Social Network Analysis (SNA), does not require model retraining for different scale networks, and can work in mesh scale networks. It has better accuracy and performance than former approaches, even getting 100% of NMI and ARS, 78% of modularity in Karate Club and 89% of NMI, 93% of ARS and 86% of modularity in Dolphin data set with the least conductance. This method is particularly useful in discovering new relations and structures in a system and it is very efficient.