mlsanityData to 7 Oct 2026

Community Detection in Networks with Node Attributes

arXiv 1401.7267 ↗First posted 28 Jan 2014cs.SI physics.soc-phcited by 44

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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.

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jan 2014.

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Citing papers per year: 2021: 12; 2022: 8; 2023: 11; 2024: 6; 2025: 4; 2026: 3.

44 papers.

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  1. 2021Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalizationcited by 70
  2. 2021A Comprehensive Survey on Community Detection with Deep Learningcited by 63
  3. 2021Rethinking Graph Auto-Encoder Models for Attributed Graph Clusteringcited by 37
  4. 2022Effects of Graph Convolutions in Multi-layer Networkscited by 24
  5. 2023A Comprehensive Review of Community Detection in Graphscited by 12
  6. 2021Effective and Scalable Clustering on Massive Attributed Graphscited by 10
  7. 2021An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guaranteescited by 10
  8. 2026Query-Aware Flow Diffusion for Graph-Based RAG with Retrieval Guaranteescited by 7
  9. 2024A Versatile Framework for Attributed Network Clustering via K-Nearest Neighbor Augmentationcited by 6
  10. 2022A Spectral Analysis of Graph Neural Networks on Dense and Sparse Graphscited by 5
  11. 2021Variational Co-embedding Learning for Attributed Network Clusteringcited by 4
  12. 2023Neural-prior stochastic block modelcited by 4
32 more
  1. 2023Uncertainty in GNN Learning Evaluations: The Importance of a Consistent Benchmark for Community Detectioncited by 4
  2. 2023A Comprehensive Survey on Graph Summarization with Graph Neural Networkscited by 3
  3. 2023Variational Disentangled Graph Auto-Encoders for Link Predictioncited by 3
  4. 2023Uncertainty in GNN Learning Evaluations: A Comparison Between Measures for Quantifying Randomness in GNN Community Detectioncited by 3
  5. 2021On the Fundamental Limits of Matrix Completion: Leveraging Hierarchical Similarity Graphscited by 2
  6. 2022Deep Graph Clustering via Mutual Information Maximization and Mixture Modelcited by 2
  7. 2022META-CODE: Community Detection via Exploratory Learning in Topologically Unknown Networkscited by 2
  8. 2023Local Graph Clustering with Noisy Labelscited by 2
  9. 2023Spectral Clustering of Attributed Multi-relational Graphscited by 2
  10. 2024Analysis of Corrected Graph Convolutionscited by 2
  11. 2021A multilevel clustering technique for community detectioncited by 1
  12. 2022Higher-order accurate two-sample network inference and network hashingcited by 1
  13. 2022Learning Optimal Graph Filters for Clustering of Attributed Graphscited by 1
  14. 2021Variational Embeddings for Community Detection and Node Representationcited by 0
  15. 2021Overlapping Community Detection in Temporal Text Networkscited by 0
  16. 2021Node metadata can produce predictability transitions in network inference problemscited by 0
  17. 2021Measuring Proximity in Attributed Networks for Community Detectioncited by 0
  18. 2022Perfect Spectral Clustering with Discrete Covariatescited by 0
  19. 2022Overlapping Community Detection using Dynamic Dilated Aggregation in Deep Residual GCNcited by 0
  20. 2023A Unified Framework for Exploratory Learning-Aided Community Detection Under Topological Uncertaintycited by 0
  21. 2023Disentangling Node Attributes from Graph Topology for Improved Generalizability in Link Predictioncited by 0
  22. 2023Unsupervised Learning via Network-Aware Embeddingscited by 0
  23. 2024Information-Theoretic Thresholds for the Alignments of Partially Correlated Graphscited by 0
  24. 2024Graph Clustering with Cross-View Feature Propagationcited by 0
  25. 2024HACD: Harnessing Attribute Semantics and Mesoscopic Structure for Community Detectioncited by 0
  26. 2024A Misclassification Network-Based Method for Comparative Genomic Analysiscited by 0
  27. 2025Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphscited by 0
  28. 2025A Local Perspective-based Model for Overlapping Community Detectioncited by 0
  29. 2025A Noise-Resilient Semi-Supervised Graph Autoencoder for Overlapping Semantic Community Detectioncited by 0
  30. 2025Covariate-assisted graph matchingcited by 0
  31. 2026Strategy-Supervised Autonomous Laparoscopic Camera Control via Event-Driven Graph Miningcited by 0
  32. 2026Diffusion-Induced Spatial Attention Overlapping Community Detectioncited by 0