# A Survey on the Recent Advances of Deep Community Detection
###### 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. deep learning; network analysis; community detection; social computing +
Footnote †: journal: Medical  
community detection can facilitate the uncovering of hidden relationships between social network users. Traditional methods of community detection are based on statistical inference, heuristic approaches, or conventional machine learning [1]. However, 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]. In conventional machine learning, community detection has been treated as a problem of forming, extracting, and verifying the accuracy of clusters on large graphs. These approaches attempt to generate a network embedding with few dimensions to reconstruct the original network. However, this type of representation to a low dimensional space is, in a broad sense, linear [8]. The fact that the real world networks include nonlinear structures makes the traditional strategies less useful. In a world where the scale of networks increases and the datasets become larger and larger, more robust and efficient techniques are required to achieve high performance [9; 10]. Deep communities in a graph are connected components that can only be located after removing nodes and edges from the rest of the graph. In this work, we are reviewing the most recent community detection approaches, which use deep learning strategies. The main contributions of our work are the following:  
1. We describe and analyze the most prominent new strategies for community detection. 2. We organize the existing schemes in groups, depending on the technique being used. 3. We outline several points of interest based on the study of each family of community detection papers. 4. We present some possible future trends. At this time, we must point out that community detection on big data networks is an ongoing field and the majority of the papers presented are quite recent. In this survey, we have selected to present quite recent papers, the majority of which have been published between 2019-2020. Still, one can expect more published papers on the topic. 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.