Machine Learning Approaches for Community Detection in Online Social Networks.

Network analysis is responsible for taking insights or generating predictions from networked data sources where community detection finds chunks of related data in a network. The importance of community detection spans in different domain applications, from social network formation to protein interaction predictions. This work compares five state-of-the-art solutions to community detection using machine learning approaches in the context of online social networks - Graph-GAN, SDNE, ComE, AC2CD, and CLARE. The experiments using real-world online social network datasets (Email-EU-Core, BlogCatalog3, Flickr) with micro-F1, macro-F1, and NMI scores demonstrate that graph neural networks and deep reinforcement learning approaches are better suited for the community detection task than others based on probabilistic or shallow networks.