A Comprehensive Survey on Community Detection with Deep Learning

Abstract

A community reveals the features and connections of its members that are different from those in other communities in a network. Detecting communities is of great significance in network analysis. Despite the classical spectral clustering and statistical inference methods, we notice a significant development of deep learning techniques for community detection in recent years with their advantages in handling high dimensional network data. Hence, a comprehensive overview of community detection's latest progress through deep learning is timely to academics and practitioners. This survey devises and proposes a new taxonomy covering different state-of-the-art methods, including deep learning-based models upon deep neural networks, deep nonnegative matrix factorization and deep sparse filtering. The main category, i.e., deep neural networks, is further divided into convolutional networks, graph attention networks, generative adversarial networks and autoencoders. The survey also summarizes the popular benchmark data sets, evaluation metrics, and open-source implementations to address experimentation settings. We then discuss the practical applications of community detection in various domains and point to implementation scenarios. Finally, we outline future directions by suggesting challenging topics in this fast-growing deep learning field.

29 Pages

References in corpus (17)

• Fast unfolding of communities in large networks
• Modularity and community structure in networks
• Cooperative Game Theory Approaches for Network Partitioning
• Maps of random walks on complex networks reveal community structure
• Near linear time algorithm to detect community structures in large-scale networks
• Benchmark graphs for testing community detection algorithms
• Stochastic blockmodels and community structure in networks
• Community detection in networks: A user guide
• Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities
• What's in a crowd? Analysis of face-to-face behavioral networks
• Detect overlapping and hierarchical community structure in networks
• Structural Deep Clustering Network
• The Slashdot Zoo: Mining a Social Network with Negative Edges
• Adaptive Graph Encoder for Attributed Graph Embedding
• Deep Learning for Community Detection: Progress, Challenges and Opportunities
• Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs
• Detecting Communities from Heterogeneous Graphs: A Context Path-based Graph Neural Network Model

Cited by in corpus (7)

• Attention-based graph neural networks: a survey
• Community detection for weighted bipartite networks
• A Local Perspective-based Model for Overlapping Community Detection
• Less is More: Simple yet Effective Heuristic Community Detection with Graph Convolution Network
• SPINEX-Clustering: Similarity-based Predictions with Explainable Neighbors Exploration for Clustering Problems
• Community detection robustness of graph neural networks
• Human-Centric Community Detection in Hybrid Metaverse Networks with Integrated AI Entities