mlsanityData to 7 Oct 2026

DGCLUSTER: A Neural Framework for Attributed Graph Clustering via Modularity Maximization

arXiv 2312.12697 ↗First posted 20 Dec 2023cs.LG cs.SIcited by 21

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Abstract (arXiv)

Graph clustering is a fundamental and challenging task in the field of graph mining where the objective is to group the nodes into clusters taking into consideration the topology of the graph. It has several applications in diverse domains spanning social network analysis, recommender systems, computer vision, and bioinformatics. In this work, we propose a novel method, DGCluster, which primarily optimizes the modularity objective using graph neural networks and scales linearly with the graph size. Our method does not require the number of clusters to be specified as a part of the input and can also leverage the availability of auxiliary node level information. We extensively test DGCluster on several real-world datasets of varying sizes, across multiple popular cluster quality metrics. Our approach consistently outperforms the state-of-the-art methods, demonstrating significant performance gains in almost all settings.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 21 of its 43 references that are in the map. First posted on arXiv 20 Dec 2023.

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21 of 43 references.

References · 21

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
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  12. 2017Self-Normalizing Neural Networkscited by 591
9 more
  1. 2019Attributed Graph Clustering: A Deep Attentional Embedding Approachcited by 149
  2. 2020Structural Deep Clustering Networkcited by 145
  3. 2016Community detection in networks: A user guidecited by 105
  4. 2019Attributed Graph Clustering via Adaptive Graph Convolutioncited by 96
  5. 2019Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learningcited by 94
  6. 2019Overlapping Community Detection with Graph Neural Networkscited by 45
  7. 2013Efficient Monte Carlo and greedy heuristic for the inference of stochastic block modelscited by 29
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 8; 2026: 8.

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  1. 2024Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspectivecited by 18
  2. 2024Node Identifiers: Compact, Discrete Representations for Efficient Graph Learningcited by 10
  3. 2024Spectral Subspace Clustering for Attributed Graphscited by 6
  4. 2025ASIL: Augmented Structural Information Learning for Deep Graph Clustering in Hyperbolic Spacecited by 4
  5. 2025Effective Clustering for Large Multi-Relational Graphscited by 2
  6. 2025Clustering-Oriented Generative Attribute Graph Imputationcited by 1
  7. 2026Community-Level Modeling of Gyral Folding Patterns for Robust and Anatomically Informed Individualized Brain Mappingcited by 1
  8. 2024Structure-enhanced Contrastive Learning for Graph Clusteringcited by 0
  9. 2024RDSA: A Robust Deep Graph Clustering Framework via Dual Soft Assignmentcited by 0
  10. 2025LGRPool: Hierarchical Graph Pooling Via Local-Global Regularisationcited by 0
  11. 2025Advancing Community Detection with Graph Convolutional Neural Networks: Bridging Topological and Attributive Cohesioncited by 0
  12. 2025FTSCommDetector: Discovering Behavioral Communities through Temporal Synchronizationcited by 0
9 more
  1. 2025FUSE: Fast Semi-Supervised Node Embedding Learning via Structural and Label-Aware Optimizationcited by 0
  2. 2025Dual Refinement Cycle Learning: Unsupervised Text Classification of Mamba and Community Detection on Text Attributed Graphcited by 0
  3. 2026Weighted Graph Clustering via Scale Contraction and Graph Structure Learningcited by 0
  4. 2026Beyond the Academic Monoculture: A Unified Framework and Industrial Perspective for Attributed Graph Clusteringcited by 0
  5. 2026A Unified Perspective for Learning Graph Representations Across Multi-Level Abstractionscited by 0
  6. 2026Graph Neural Networks for Community Detection in Graph Signal Analysiscited by 0
  7. 2026Fast and Featureless Node Representation Learning with Partial Pairwise Supervisioncited by 0
  8. 2026AdaSTORM: Scaling LLM Reasoning on Dynamic Graphs via Adaptive Spatio-Temporal Multi-Agent Collaborationcited by 0
  9. 2026ComNetX: Local Hierarchical Adaptation for Dynamic Community Detectioncited by 0