# A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning

arXiv: https://arxiv.org/abs/2101.01669 · HTML: https://mlsanity.com/paper/2101.01669/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 174 references that are in the map.
- First posted on arXiv 3 Jan 2021.

## Builds on (most cited first, 30 of 37)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Community Detection and Stochastic Block Models](https://mlsanity.com/paper/1703.10146/) (2017)
- [Attributed Graph Clustering: A Deep Attentional Embedding Approach](https://mlsanity.com/paper/1906.06532/) (2019)
- [Cooperative Game Theory Approaches for Network Partitioning](https://mlsanity.com/paper/1707.03587/) (2017)
- [GMNN: Graph Markov Neural Networks](https://mlsanity.com/paper/1905.06214/) (2019)
- [A Review of Stochastic Block Models and Extensions for Graph Clustering](https://mlsanity.com/paper/1903.00114/) (2019)
- [Overlapping Community Detection with Graph Neural Networks](https://mlsanity.com/paper/1909.12201/) (2019)
- [Deep Learning for Community Detection: Progress, Challenges and Opportunities](https://mlsanity.com/paper/2005.08225/) (2020)
- [Stochastic Blockmodels meet Graph Neural Networks](https://mlsanity.com/paper/1905.05738/) (2019)
- [Clustering and Community Detection in Directed Networks: A Survey](https://mlsanity.com/paper/1308.0971/) (2013)
- [vGraph: A Generative Model for Joint Community Detection and Node Representation Learning](https://mlsanity.com/paper/1906.07159/) (2019)
- [Magnetic eigenmaps for community detection in directed networks](https://mlsanity.com/paper/1606.07359/) (2016)
- [Network Cross-Validation for Determining the Number of Communities in Network Data](https://mlsanity.com/paper/1411.1715/) (2014)
- [Dynamic stochastic blockmodels for time-evolving social networks](https://mlsanity.com/paper/1403.0921/) (2014)
- [Mixture models with a prior on the number of components](https://mlsanity.com/paper/1502.06241/) (2015)
- [Scalable detection of statistically significant communities and hierarchies, using message-passing for modularity](https://mlsanity.com/paper/1403.5787/) (2014)
- [Convexified Modularity Maximization for Degree-corrected Stochastic Block Models](https://mlsanity.com/paper/1512.08425/) (2015)
- [CommunityGAN: Community Detection with Generative Adversarial Nets](https://mlsanity.com/paper/1901.06631/) (2019)
- [Uncovering the Small Community Structure in Large Networks: A Local Spectral Approach](https://mlsanity.com/paper/1509.07715/) (2015)
- [Change Point Estimation in a Dynamic Stochastic Block Model](https://mlsanity.com/paper/1812.03090/) (2018)
- [A Tensor Approach to Learning Mixed Membership Community Models](https://mlsanity.com/paper/1302.2684/) (2013)
- [Graph Clustering with Dynamic Embedding](https://mlsanity.com/paper/1712.08249/) (2017)
- [Community-preserving Graph Convolutions for Structural and Functional Joint Embedding of Brain Networks](https://mlsanity.com/paper/1911.03583/) (2019)
- [Quaternion Collaborative Filtering for Recommendation](https://mlsanity.com/paper/1906.02594/) (2019)
- [MEGAN: A Generative Adversarial Network for Multi-View Network Embedding](https://mlsanity.com/paper/1909.01084/) (2019)
- [Stochastic Block Transition Models for Dynamic Networks](https://mlsanity.com/paper/1411.5404/) (2014)
- [A spectral method for community detection in moderately-sparse degree-corrected stochastic block models](https://mlsanity.com/paper/1506.08621/) (2015)
- [Efficient Correlated Topic Modeling with Topic Embedding](https://mlsanity.com/paper/1707.00206/) (2017)

## Built on by (most cited first, 30 of 55)

- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [Graph Self-Supervised Learning: A Survey](https://mlsanity.com/paper/2103.00111/) (2021)
- [Trustworthy Graph Neural Networks: Aspects, Methods and Trends](https://mlsanity.com/paper/2205.07424/) (2022)
- [A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions](https://mlsanity.com/paper/2206.07579/) (2022)
- [Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection](https://mlsanity.com/paper/2108.09896/) (2021)
- [A Comprehensive Survey on Community Detection with Deep Learning](https://mlsanity.com/paper/2105.12584/) (2021)
- [Anomaly Detection in Dynamic Graphs via Transformer](https://mlsanity.com/paper/2106.09876/) (2021)
- [Block Modeling-Guided Graph Convolutional Neural Networks](https://mlsanity.com/paper/2112.13507/) (2021)
- [T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation](https://mlsanity.com/paper/2212.12738/) (2022)
- [Geometry Interaction Knowledge Graph Embeddings](https://mlsanity.com/paper/2206.12418/) (2022)
- [Detecting Communities from Heterogeneous Graphs: A Context Path-based Graph Neural Network Model](https://mlsanity.com/paper/2109.02058/) (2021)
- [Powerful Graph Convolutioal Networks with Adaptive Propagation Mechanism for Homophily and Heterophily](https://mlsanity.com/paper/2112.13562/) (2021)
- [Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation](https://mlsanity.com/paper/2305.05848/) (2023)
- [A Comprehensive Review of Community Detection in Graphs](https://mlsanity.com/paper/2309.11798/) (2023)
- [TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature](https://mlsanity.com/paper/2205.12784/) (2022)
- [KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks](https://mlsanity.com/paper/2302.11396/) (2023)
- [A Survey on Deep Clustering: From the Prior Perspective](https://mlsanity.com/paper/2406.19602/) (2024)
- [CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning](https://mlsanity.com/paper/2205.15083/) (2022)
- [Does GCL Need a Large Number of Negative Samples? Enhancing Graph Contrastive Learning with Effective and Efficient Negative Sampling](https://mlsanity.com/paper/2503.17908/) (2025)
- [Variational Graph Generator for Multi-View Graph Clustering](https://mlsanity.com/paper/2210.07011/) (2022)
- [Multitask Active Learning for Graph Anomaly Detection](https://mlsanity.com/paper/2401.13210/) (2024)
- [AGMN: Association Graph-based Graph Matching Network for Coronary Artery Semantic Labeling on Invasive Coronary Angiograms](https://mlsanity.com/paper/2301.04733/) (2023)
- [Node Embedding using Mutual Information and Self-Supervision based Bi-level Aggregation](https://mlsanity.com/paper/2104.13014/) (2021)
- [A Survey on Role-Oriented Network Embedding](https://mlsanity.com/paper/2107.08379/) (2021)
- [ER: Equivariance Regularizer for Knowledge Graph Completion](https://mlsanity.com/paper/2206.12142/) (2022)
- [Bipartite mixed membership distribution-free model. A novel model for community detection in overlapping bipartite weighted networks](https://mlsanity.com/paper/2211.00912/) (2022)
- [Uncertainty in GNN Learning Evaluations: The Importance of a Consistent Benchmark for Community Detection](https://mlsanity.com/paper/2305.06026/) (2023)
- [Uncertainty in GNN Learning Evaluations: A Comparison Between Measures for Quantifying Randomness in GNN Community Detection](https://mlsanity.com/paper/2312.09015/) (2023)
- [Evading Community Detection via Counterfactual Neighborhood Search](https://mlsanity.com/paper/2310.08909/) (2023)
- [Community-Centric Graph Unlearning](https://mlsanity.com/paper/2408.09705/) (2024)

## Abstract (arXiv)

Community detection, a fundamental task for network analysis, aims to partition a network into multiple sub-structures to help reveal their latent functions. Community detection has been extensively studied in and broadly applied to many real-world network problems. Classical approaches to community detection typically utilize probabilistic graphical models and adopt a variety of prior knowledge to infer community structures. As the problems that network methods try to solve and the network data to be analyzed become increasingly more sophisticated, new approaches have also been proposed and developed, particularly those that utilize deep learning and convert networked data into low dimensional representation. Despite all the recent advancement, there is still a lack of insightful understanding of the theoretical and methodological underpinning of community detection, which will be critically important for future development of the area of network analysis. In this paper, we develop and present a unified architecture of network community-finding methods to characterize the state-of-the-art of the field of community detection. Specifically, we provide a comprehensive review of the existing community detection methods and introduce a new taxonomy that divides the existing methods into two categories, namely probabilistic graphical model and deep learning. We then discuss in detail the main idea behind each method in the two categories. Furthermore, to promote future development of community detection, we release several benchmark datasets from several problem domains and highlight their applications to various network analysis tasks. We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.