Abstract
In the first days of social networking, the typical view of a community was a set of user profiles of the same interests and likes, and this community kept enlarging by searching, proposing, and adding new members with the same characteristics that were likely to interfere with the existing members. Today, things have changed dramatically. Social networking platforms are not restricted to forming similar user profiles: The vast amounts of data produced every day have given opportunities to predict and suggest relationships, behaviors, and everyday activities like shopping, food, traveling destinations, etc. Every day, vast data amounts are generated by the famous social networks such as Facebook, Twitter, Instagram, and so on. For example, Facebook alone generates 4 petabytes of data per day. The analysis of such data is of high importance to many aspects like recommendation systems, businesses, health organizations, etc. The community detection problem received considerable attention a long time ago. Communities are represented as clusters of an entire network. Most of the community detection techniques are based on graph structures. In this paper, we present the recent advances of deep learning techniques for community detection. We describe the most recent strategies presented in this field, and we provide some general discussion and some future trends.
Highlights
IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
The deep community detection strategies can be divided into three big families: (i) AE-Based Community Detection (Auto-encoders are used), (ii) CNN/GNNBased Community Detection (Convolutional Neural Networks (CNNs) and their variant
In the remainder of this subsection, we present the most recent CNN and GNN approaches to community detection
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Traditional methods of community detection are based on statistical inference, heuristic approaches, or conventional machine learning [1] Despite their past popularity, these strategies are not efficient in the modern era, where the datasets are larger, more complex, and the social relationships over the networks are becoming more complex and much more difficult to define and extract [2,3,4,5,6,7]. A quite recent review [1] in 2020 briefly discusses only a few of the papers which are presented in this survey, while only future trends are presented (there is no detailed commenting on the presented schemes) In another survey [11], the authors introduce a new taxonomy that divides the existing methods into two categories, namely probabilistic graphical models and deep learning.
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