# Deep Learning for Community Detection: Progress, Challenges and Opportunities

arXiv: https://arxiv.org/abs/2005.08225 · HTML: https://mlsanity.com/paper/2005.08225/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2020.

## Built on by (most cited first, 30 of 42)

- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions](https://mlsanity.com/paper/2206.07579/) (2022)
- [A Comprehensive Survey on Community Detection with Deep Learning](https://mlsanity.com/paper/2105.12584/) (2021)
- [A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning](https://mlsanity.com/paper/2101.01669/) (2021)
- [Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities](https://mlsanity.com/paper/2204.07321/) (2022)
- [Graph Communal Contrastive Learning](https://mlsanity.com/paper/2110.14863/) (2021)
- [Graph Transformers: A Survey](https://mlsanity.com/paper/2407.09777/) (2024)
- [A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource](https://mlsanity.com/paper/2211.12875/) (2022)
- [GLCC: A General Framework for Graph-Level Clustering](https://mlsanity.com/paper/2210.11879/) (2022)
- [Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective](https://mlsanity.com/paper/2207.11311/) (2022)
- [A Survey on Deep Learning Event Extraction: Approaches and Applications](https://mlsanity.com/paper/2107.02126/) (2021)
- [Parallel Spatio-Temporal Attention-Based TCN for Multivariate Time Series Prediction](https://mlsanity.com/paper/2203.00971/) (2022)
- [Deep reinforcement learning guided graph neural networks for brain network analysis](https://mlsanity.com/paper/2203.10093/) (2022)
- [Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges](https://mlsanity.com/paper/2203.12852/) (2022)
- [A Comprehensive Review of Community Detection in Graphs](https://mlsanity.com/paper/2309.11798/) (2023)
- [Curvature Graph Generative Adversarial Networks](https://mlsanity.com/paper/2203.01604/) (2022)
- [Redundancy-Free Self-Supervised Relational Learning for Graph Clustering](https://mlsanity.com/paper/2309.04694/) (2023)
- [Transferring Knowledge Distillation for Multilingual Social Event Detection](https://mlsanity.com/paper/2108.03084/) (2021)
- [Multivariate Anomaly Detection based on Prediction Intervals Constructed using Deep Learning](https://mlsanity.com/paper/2110.03393/) (2021)
- [State of the Art and Potentialities of Graph-level Learning](https://mlsanity.com/paper/2301.05860/) (2023)
- [Event Extraction by Associating Event Types and Argument Roles](https://mlsanity.com/paper/2108.10038/) (2021)
- [Relational Prompt-based Pre-trained Language Models for Social Event Detection](https://mlsanity.com/paper/2404.08263/) (2024)
- [A Survey on Role-Oriented Network Embedding](https://mlsanity.com/paper/2107.08379/) (2021)
- [A Comprehensive Survey on Graph Summarization with Graph Neural Networks](https://mlsanity.com/paper/2302.06114/) (2023)
- [Uncertainty in GNN Learning Evaluations: The Importance of a Consistent Benchmark for Community Detection](https://mlsanity.com/paper/2305.06026/) (2023)
- [Contrastive Deep Nonnegative Matrix Factorization for Community Detection](https://mlsanity.com/paper/2311.02357/) (2023)
- [Learning-Based Link Anomaly Detection in Continuous-Time Dynamic Graphs](https://mlsanity.com/paper/2405.18050/) (2024)
- [Uncertainty in GNN Learning Evaluations: A Comparison Between Measures for Quantifying Randomness in GNN Community Detection](https://mlsanity.com/paper/2312.09015/) (2023)
- [Self-Supervised Deep Graph Embedding with High-Order Information Fusion for Community Discovery](https://mlsanity.com/paper/2102.03302/) (2021)
- [Towards Modularity Optimization Using Reinforcement Learning to Community Detection in Dynamic Social Networks](https://mlsanity.com/paper/2111.15623/) (2021)

## Abstract (arXiv)

As communities represent similar opinions, similar functions, similar purposes, etc., community detection is an important and extremely useful tool in both scientific inquiry and data analytics. However, the classic methods of community detection, such as spectral clustering and statistical inference, are falling by the wayside as deep learning techniques demonstrate an increasing capacity to handle high-dimensional graph data with impressive performance. Thus, a survey of current progress in community detection through deep learning is timely. Structured into three broad research streams in this domain - deep neural networks, deep graph embedding, and graph neural networks, this article summarizes the contributions of the various frameworks, models, and algorithms in each stream along with the current challenges that remain unsolved and the future research opportunities yet to be explored.