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GEMSEC: Graph Embedding with Self Clustering

arXiv 1802.03997 ↗First posted 12 Feb 2018cs.SIcited by 61

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Abstract (arXiv)

Modern graph embedding procedures can efficiently process graphs with millions of nodes. In this paper, we propose GEMSEC -- a graph embedding algorithm which learns a clustering of the nodes simultaneously with computing their embedding. GEMSEC is a general extension of earlier work in the domain of sequence-based graph embedding. GEMSEC places nodes in an abstract feature space where the vertex features minimize the negative log-likelihood of preserving sampled vertex neighborhoods, and it incorporates known social network properties through a machine learning regularization. We present two new social network datasets and show that by simultaneously considering the embedding and clustering problems with respect to social properties, GEMSEC extracts high-quality clusters competitive with or superior to other community detection algorithms. In experiments, the method is found to be computationally efficient and robust to the choice of hyperparameters.

Key facts. Cited by 61 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Feb 2018.

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Citing papers per year: 2021: 12; 2022: 13; 2023: 11; 2024: 7; 2025: 14; 2026: 4.

61 papers.

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  1. 2021Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddingscited by 48
  2. 2022A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resourcecited by 26
  3. 2021Network Representation Learning: From Preprocessing, Feature Extraction to Node Embeddingcited by 19
  4. 2022VGAER: Graph Neural Network Reconstruction based Community Detectioncited by 14
  5. 2022An Invertible Graph Diffusion Neural Network for Source Localizationcited by 14
  6. 2022Learning Sparse Graphon Mean Field Gamescited by 14
  7. 2021EigenGame Unloaded: When playing games is better than optimizingcited by 13
  8. 2023A Comprehensive Review of Community Detection in Graphscited by 12
  9. 2024TEG-DB: A Comprehensive Dataset and Benchmark of Textual-Edge Graphscited by 9
  10. 2022BeGin: Extensive Benchmark Scenarios and An Easy-to-use Framework for Graph Continual Learningcited by 8
  11. 2023DINE: Dimensional Interpretability of Node Embeddingscited by 8
  12. 2023Finding Heterophilic Neighbors via Confidence-based Subgraph Matching for Semi-supervised Node Classificationcited by 7
38 more
  1. 2023Bayesian Optimisation of Functions on Graphscited by 7
  2. 2023Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphscited by 7
  3. 2022Network Shuffling: Privacy Amplification via Random Walkscited by 6
  4. 2022Bayan Algorithm: Detecting Communities in Networks Through Exact and Approximate Optimization of Modularitycited by 5
  5. 2022Realistic Synthetic Social Networks with Graph Neural Networkscited by 5
  6. 2023Community Detection Using Revised Medoid-Shift Based on KNNcited by 5
  7. 2021Low Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networkscited by 4
  8. 2021Multiple Kernel Representation Learning on Networkscited by 3
  9. 2021A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methodscited by 2
  10. 2022Fast Community Detection based on Graph Autoencoder Reconstructioncited by 2
  11. 2023Improving Signed Propagation for Graph Neural Networks in Multi-Class Environmentscited by 2
  12. 2023CONTAIN: A Community-based Algorithm for Network Immunizationcited by 2
  13. 2025OpenGT: A Comprehensive Benchmark For Graph Transformerscited by 2
  14. 2025Sheaf Graph Neural Networks via PAC-Bayes Spectral Optimizationcited by 2
  15. 2021Transfer Learning for Node Regression Applied to Spreading Predictioncited by 1
  16. 2021A Framework for Joint Unsupervised Learning of Cluster-Aware Embedding for Heterogeneous Networkscited by 1
  17. 2021Convex Joint Graph Matching and Clustering via Semidefinite Relaxationscited by 1
  18. 2022Large-scale multi-objective influence maximisation with network downscalingcited by 1
  19. 2023Learning distributed representations with efficient SoftMax normalizationcited by 1
  20. 2023Torsion Graph Neural Networkscited by 1
  21. 2024Variational Graph Auto-Encoder Based Inductive Learning Method for Semi-Supervised Classificationcited by 1
  22. 2024Disentangled and Self-Explainable Node Representation Learningcited by 1
  23. 2025Hierarchical Uncertainty-Aware Graph Neural Networkcited by 1
  24. 2025Hybrid Graph Embeddings and Louvain Algorithm for Unsupervised Community Detectioncited by 1
  25. 2025Gauge-Equivariant Graph Networks via Self-Interference Cancellationcited by 1
  26. 2026Gaussian Process Limit Reveals Structural Benefits of Graph Transformerscited by 1
  27. 2026Cluster Attention for Graph Machine Learningcited by 1
  28. 2021Variational Embeddings for Community Detection and Node Representationcited by 0
  29. 2021fMBN-E: Efficient Unsupervised Network Structure Ensemble and Selection for Clusteringcited by 0
  30. 2021Topic-aware latent models for representation learning on networkscited by 0
  31. 2022Edge Augmentation on Disconnected Graphs via Eigenvalue Elevationcited by 0
  32. 2022MGTCOM: Community Detection in Multimodal Graphscited by 0
  33. 2022Mitigating Overfitting in Graph Neural Networks via Feature and Hyperplane Perturbationcited by 0
  34. 2023PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networkscited by 0
  35. 2024Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Networkcited by 0
  36. 2024Optimally Improving Cooperative Learning in a Social Settingcited by 0
  37. 2024Better Not to Propagate: Understanding Edge Uncertainty and Over-smoothing in Signed Graph Neural Networkscited by 0
  38. 2024Network Causal Effect Estimation In Graphical Models Of Contagion And Latent Confoundingcited by 0

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