mlsanityData to 14 Oct 2026

Deep Temporal Graph Clustering

arXiv 2305.10738 ↗First posted 18 May 2023cs.LG cs.AIcited by 28

Not on the map. Why?

Abstract (arXiv)

Deep graph clustering has recently received significant attention due to its ability to enhance the representation learning capabilities of models in unsupervised scenarios. Nevertheless, deep clustering for temporal graphs, which could capture crucial dynamic interaction information, has not been fully explored. It means that in many clustering-oriented real-world scenarios, temporal graphs can only be processed as static graphs. This not only causes the loss of dynamic information but also triggers huge computational consumption. To solve the problem, we propose a general framework for deep Temporal Graph Clustering called TGC, which introduces deep clustering techniques to suit the interaction sequence-based batch-processing pattern of temporal graphs. In addition, we discuss differences between temporal graph clustering and static graph clustering from several levels. To verify the superiority of the proposed framework TGC, we conduct extensive experiments. The experimental results show that temporal graph clustering enables more flexibility in finding a balance between time and space requirements, and our framework can effectively improve the performance of existing temporal graph learning methods. The code is released: https://github.com/MGitHubL/Deep-Temporal-Graph-Clustering.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). Builds on 32 of its 80 references that are in the map. First posted on arXiv 18 May 2023.

Builds on

32 of 80 references.

References · 32

  1. 2017Inductive Representation Learning on Large Graphscited by 6,320
  2. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
  3. 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
  4. 2014DeepWalk: Online Learning of Social Representationscited by 1,547
  5. 2016Variational Graph Auto-Encoderscited by 1,382
  6. 2020Contrastive Multi-View Representation Learning on Graphscited by 652
  7. 2020Temporal Graph Networks for Deep Learning on Dynamic Graphscited by 530
  8. 2020Inductive Representation Learning on Temporal Graphscited by 462
  9. 2019Predicting Dynamic Embedding Trajectory in Temporal Interaction Networkscited by 448
  10. 2019EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphscited by 447
  11. 2021Inductive Representation Learning in Temporal Networks via Causal Anonymous Walkscited by 193
  12. 2019Attributed Graph Clustering: A Deep Attentional Embedding Approachcited by 149
20 more
  1. 2020Structural Deep Clustering Networkcited by 145
  2. 2017A Survey on Network Embeddingcited by 142
  3. 2022A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodalcited by 106
  4. 2021Deep Graph Clustering via Dual Correlation Reductioncited by 97
  5. 2020Adaptive Graph Encoder for Attributed Graph Embeddingcited by 89
  6. 2020Deep Fusion Clustering Networkcited by 71
  7. 2015Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveyscited by 60
  8. 2019Temporal Network Embedding with Micro- and Macro-dynamicscited by 52
  9. 2022TREND: TempoRal Event and Node Dynamics for Graph Representation Learningcited by 50
  10. 2022Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structurecited by 43
  11. 2015Statistical clustering of temporal networks through a dynamic stochastic block modelcited by 30
  12. 2023Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignmentcited by 25
  13. 2022CGC: Contrastive Graph Clustering for Community Detection and Trackingcited by 21
  14. 2021Inductive Representation Learning in Temporal Networks via Mining Neighborhood and Community Influencescited by 19
  15. 2020Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networkscited by 11
  16. 2021On the Equivalence Between Temporal and Static Graph Representations for Observational Predictionscited by 10
  17. 2017Graph Clustering with Dynamic Embeddingcited by 9
  18. 2023SARF: Aliasing Relation Assisted Self-Supervised Learning for Few-shot Relation Reasoningcited by 9
  19. 2021DyGCN: Dynamic Graph Embedding with Graph Convolutional Networkcited by 6
  20. 2021Dynamic Structural Clustering on Graphscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 10; 2024: 9; 2025: 6; 2026: 3.

28 papers.

Sort:

By area:

  1. 2023Dink-Net: Neural Clustering on Large Graphscited by 38
  2. 2023Fast Continual Multi-View Clustering with Incomplete Viewscited by 19
  3. 2024DyG-Mamba: Continuous State Space Modeling on Dynamic Graphscited by 17
  4. 2023Reinforcement Graph Clustering with Unknown Cluster Numbercited by 16
  5. 2023One-step Multi-view Clustering with Diverse Representationcited by 10
  6. 2025FreCT: Frequency-augmented Convolutional Transformer for Robust Time Series Anomaly Detectioncited by 10
  7. 2023TMac: Temporal Multi-Modal Graph Learning for Acoustic Event Classificationcited by 9
  8. 2023PUMA: Efficient Continual Graph Learning for Node Classification with Graph Condensationcited by 9
  9. 2023Scalable Incomplete Multi-View Clustering with Structure Alignmentcited by 7
  10. 2024Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspectivecited by 7
  11. 2023Efficient Multi-View Graph Clustering with Local and Global Structure Preservationcited by 5
  12. 2024Negative-Free Self-Supervised Gaussian Embedding of Graphscited by 5
16 more
  1. 2023Contrastive Continual Multi-view Clustering with Filtered Structural Fusioncited by 4
  2. 2025A Survey on Temporal Interaction Graph Representation Learning: Progress, Challenges, and Opportunitiescited by 4
  3. 2023arXiv4TGC: Large-Scale Datasets for Temporal Graph Clusteringcited by 3
  4. 2025K*-Means: A Parameter-free Clustering Algorithmcited by 3
  5. 2024Scalable and Efficient Temporal Graph Representation Learning via Forward Recent Samplingcited by 2
  6. 2025Multi-Relation Graph-Kernel Strengthen Network for Graph-Level Clusteringcited by 1
  7. 2026Deep Temporal Graph Clustering: A Comprehensive Benchmark and Datasetscited by 1
  8. 2026Beyond the Academic Monoculture: A Unified Framework and Industrial Perspective for Attributed Graph Clusteringcited by 1
  9. 2026Decoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Modelscited by 1
  10. 2024One-Step Late Fusion Multi-view Clustering with Compressed Subspacecited by 0
  11. 2024A General Benchmark Framework is Dynamic Graph Neural Network Needcited by 0
  12. 2024S2RC-GCN: A Spatial-Spectral Reliable Contrastive Graph Convolutional Network for Complex Land Cover Classification Using Hyperspectral Imagescited by 0
  13. 2024Self-Supervised Contrastive Graph Clustering Network via Structural Information Fusioncited by 0
  14. 2024Federated Temporal Graph Clusteringcited by 0
  15. 2025Unfolded Laplacian Spectral Embedding: A Theoretically Grounded Approach to Dynamic Network Representationcited by 0
  16. 2025FTSCommDetector: Discovering Behavioral Communities through Temporal Synchronizationcited by 0