# Community Detection in Networks with Node Attributes

arXiv: https://arxiv.org/abs/1401.7267 · HTML: https://mlsanity.com/paper/1401.7267/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2014.

## Built on by (most cited first, 30 of 44)

- [Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization](https://mlsanity.com/paper/2102.06966/) (2021)
- [A Comprehensive Survey on Community Detection with Deep Learning](https://mlsanity.com/paper/2105.12584/) (2021)
- [Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering](https://mlsanity.com/paper/2107.08562/) (2021)
- [Effects of Graph Convolutions in Multi-layer Networks](https://mlsanity.com/paper/2204.09297/) (2022)
- [A Comprehensive Review of Community Detection in Graphs](https://mlsanity.com/paper/2309.11798/) (2023)
- [Effective and Scalable Clustering on Massive Attributed Graphs](https://mlsanity.com/paper/2102.03826/) (2021)
- [An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees](https://mlsanity.com/paper/2112.10467/) (2021)
- [Query-Aware Flow Diffusion for Graph-Based RAG with Retrieval Guarantees](https://mlsanity.com/paper/2605.18775/) (2026)
- [A Versatile Framework for Attributed Network Clustering via K-Nearest Neighbor Augmentation](https://mlsanity.com/paper/2408.05459/) (2024)
- [A Spectral Analysis of Graph Neural Networks on Dense and Sparse Graphs](https://mlsanity.com/paper/2211.03231/) (2022)
- [Variational Co-embedding Learning for Attributed Network Clustering](https://mlsanity.com/paper/2104.07295/) (2021)
- [A Comprehensive Survey on Graph Summarization with Graph Neural Networks](https://mlsanity.com/paper/2302.06114/) (2023)
- [Neural-prior stochastic block model](https://mlsanity.com/paper/2303.09995/) (2023)
- [Uncertainty in GNN Learning Evaluations: The Importance of a Consistent Benchmark for Community Detection](https://mlsanity.com/paper/2305.06026/) (2023)
- [Variational Disentangled Graph Auto-Encoders for Link Prediction](https://mlsanity.com/paper/2306.11315/) (2023)
- [Uncertainty in GNN Learning Evaluations: A Comparison Between Measures for Quantifying Randomness in GNN Community Detection](https://mlsanity.com/paper/2312.09015/) (2023)
- [On the Fundamental Limits of Matrix Completion: Leveraging Hierarchical Similarity Graphs](https://mlsanity.com/paper/2109.05408/) (2021)
- [Deep Graph Clustering via Mutual Information Maximization and Mixture Model](https://mlsanity.com/paper/2205.05168/) (2022)
- [META-CODE: Community Detection via Exploratory Learning in Topologically Unknown Networks](https://mlsanity.com/paper/2208.11015/) (2022)
- [Local Graph Clustering with Noisy Labels](https://mlsanity.com/paper/2310.08031/) (2023)
- [Spectral Clustering of Attributed Multi-relational Graphs](https://mlsanity.com/paper/2311.01840/) (2023)
- [Analysis of Corrected Graph Convolutions](https://mlsanity.com/paper/2405.13987/) (2024)
- [A multilevel clustering technique for community detection](https://mlsanity.com/paper/2101.06551/) (2021)
- [Higher-order accurate two-sample network inference and network hashing](https://mlsanity.com/paper/2208.07573/) (2022)
- [Learning Optimal Graph Filters for Clustering of Attributed Graphs](https://mlsanity.com/paper/2211.04634/) (2022)
- [Variational Embeddings for Community Detection and Node Representation](https://mlsanity.com/paper/2101.03885/) (2021)
- [Overlapping Community Detection in Temporal Text Networks](https://mlsanity.com/paper/2101.05137/) (2021)
- [Node metadata can produce predictability transitions in network inference problems](https://mlsanity.com/paper/2103.14424/) (2021)
- [Measuring Proximity in Attributed Networks for Community Detection](https://mlsanity.com/paper/2111.03089/) (2021)
- [Perfect Spectral Clustering with Discrete Covariates](https://mlsanity.com/paper/2205.08047/) (2022)

## Abstract (arXiv)

Community detection algorithms are fundamental tools that allow us to uncover organizational principles in networks. When detecting communities, there are two possible sources of information one can use: the network structure, and the features and attributes of nodes. Even though communities form around nodes that have common edges and common attributes, typically, algorithms have only focused on one of these two data modalities: community detection algorithms traditionally focus only on the network structure, while clustering algorithms mostly consider only node attributes. In this paper, we develop Communities from Edge Structure and Node Attributes (CESNA), an accurate and scalable algorithm for detecting overlapping communities in networks with node attributes. CESNA statistically models the interaction between the network structure and the node attributes, which leads to more accurate community detection as well as improved robustness in the presence of noise in the network structure. CESNA has a linear runtime in the network size and is able to process networks an order of magnitude larger than comparable approaches. Last, CESNA also helps with the interpretation of detected communities by finding relevant node attributes for each community.