… As compared with the modularity method [12] and the L1 norm subgraph detection … deep
community detection performance. We illustrate the proposed deep community detection …

… To structure this survey, we devised a taxonomy for deep community detection methods
according to the iconic characteristics of the employed deep learning models. The taxonomy …

… a deep community detection method which includes (1) matrix reconstruction method, (2)
spatial feature extraction method and (3) community detection … a deep community detection …

… Communities are represented as clusters of an entire network. Most of the community detection
… the recent advances of deep learning techniques for community detection. We describe …

… on new trends in deep learning for community detection. Our … progress in deep learning for
community detection. Each … approaches, being deep neural networks, deep graph embedding…

… review of the existing community detection methods and … for community detection, ie, from
statistical modeling to deep … [39], statistical inference [40] or deep learning [41]. Third, we …

… , however, deep neural networks have not yet been successfully applied to community
detection. Taking advantage of the nonlinear representation power of deep neural networks, we …

… structured deep convolutional neural network (CNN) model to better detect communities
in … show the effectiveness and robustness of our structured deep model on a variety of real-world …

… recommendation system based on deep social community detection and user popularity is
… innovative deep community detection approach based on feature learning and deep neural …

… the community detection problems in attributed graphs. Given an attributed graph 𝐺 and the
number of communities 𝐾, the community detection … to the 𝑘-th community. The partition of …