A Comprehensive Survey on Community Detection With Deep Learning

By Xing Su, Shan Xue, Fanzhen Liu, Jia Wu, Jian Yang, Chuan Zhou, Wenbin Hu, Cécile Paris, Surya Nepal, Di Jin and 2 more. IEEE Transactions on Neural Networks and Learning Systems, 2021

454 citations recorded by the source corpus.

Detecting a community in a network is a matter of discerning the distinct features and connections of a group of members that are different from those in other communities. The ability to do this is of great significance in network analysis. However, beyond the classic spectral clustering and statistical inference methods, there have been significant developments with deep learning techniques for community detection in recent years—particularly when it comes to handling high-dimensional network data. Hence, a comprehensive review of the latest progress in community detection through deep learning is timely. To frame the survey, we have devised a new taxonomy covering different state-of-the-art methods, including deep learning models based on deep neural networks (DNNs), deep nonnegative matrix factorization, and deep sparse filtering. The main category, i.e., DNNs, is further divided into convolutional networks, graph attention networks, generative adversarial networks, and autoencoders. The popular benchmark datasets, evaluation metrics, and open-source implementations to address experimentation settings are also summarized. This is followed by a discussion on the practical applications of community detection in various domains. The survey concludes with suggestions of challenging topics that would make for fruitful future research directions in this fast-growing deep learning field.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/f0c27af6c330d5c3b0a8eb376a69ce92c85badd7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Hui Xiong
2. Jia Wu
3. Quan Z. Sheng
4. Chuan Zhou
5. Shan Xue
6. Xiaoxiao Ma
7. Jian Yang

In Statistical and Nonlinear Physics

1. Hui Xiong
2. Jia Wu
3. Quan Z. Sheng
4. Chuan Zhou
5. Shan Xue
6. Xiaoxiao Ma
7. Jian Yang

Citations in the corpus

• A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications (2023, methodology): “The DL uses geographies and calculations for a vast range of problems [142, 143].”
• A Comprehensive Survey on Graph Anomaly Detection With Deep Learning (2021, background): “The more recent studies, such as deep graph representation learning and graph neural networks (GNNs), further enrich the capability of deep learning for graph data mining [58], [59], [60], [61], [62].”