mlsanityData to 7 Oct 2026

Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs

arXiv 2501.11880 ↗First posted 21 Jan 2025cs.LG cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Dynamic graph representation learning plays a crucial role in understanding evolving behaviors. However, existing methods often struggle with flexibility, adaptability, and the preservation of temporal and structural dynamics. To address these issues, we propose Community-aware Temporal Walks (CTWalks), a novel framework for representation learning on continuous-time dynamic graphs. CTWalks integrates three key components: a community-based parameter-free temporal walk sampling mechanism, an anonymization strategy enriched with community labels, and an encoding process that leverages continuous temporal dynamics modeled via ordinary differential equations (ODEs). This design enables precise modeling of both intra- and inter-community interactions, offering a fine-grained representation of evolving temporal patterns in continuous-time dynamic graphs. CTWalks theoretically overcomes locality bias in walks and establishes its connection to matrix factorization. Experiments on benchmark datasets demonstrate that CTWalks outperforms established methods in temporal link prediction tasks, achieving higher accuracy while maintaining robustness.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 36 of its 60 references that are in the map. First posted on arXiv 21 Jan 2025.

Builds on

36 of 60 references.

References · 36

  1. 2017Inductive Representation Learning on Large Graphscited by 6,284
  2. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  3. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  4. 2018Neural Ordinary Differential Equationscited by 3,733
  5. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  6. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  7. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  8. 2015LINE: Large-scale Information Network Embeddingcited by 582
  9. 2016Recurrent Neural Networks for Multivariate Time Series with Missing Valuescited by 548
  10. 2020Temporal Graph Networks for Deep Learning on Dynamic Graphscited by 521
  11. 2020Inductive Representation Learning on Temporal Graphscited by 454
  12. 2019EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphscited by 443
24 more
  1. 2019Predicting Dynamic Embedding Trajectory in Temporal Interaction Networkscited by 443
  2. 2019KGAT: Knowledge Graph Attention Network for Recommendationcited by 342
  3. 2015A Review of Relational Machine Learning for Knowledge Graphscited by 264
  4. 2019Representation Learning for Dynamic Graphs: A Surveycited by 260
  5. 2015A Critical Review of Recurrent Neural Networks for Sequence Learningcited by 223
  6. 2017struc2vec: Learning Node Representations from Structural Identitycited by 214
  7. 2017Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2veccited by 197
  8. 2021Inductive Representation Learning in Temporal Networks via Causal Anonymous Walkscited by 188
  9. 2023Towards Better Dynamic Graph Learning: New Architecture and Unified Librarycited by 154
  10. 2022ROLAND: Graph Learning Framework for Dynamic Graphscited by 131
  11. 2019Continuous Graph Neural Networkscited by 114
  12. 2018Learning Tree-based Deep Model for Recommender Systemscited by 99
  13. 2020Neural Spatio-Temporal Point Processescited by 92
  14. 2018dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learningcited by 88
  15. 2022Multivariate Time Series Forecasting with Dynamic Graph Neural ODEscited by 72
  16. 2022Provably expressive temporal graph networkscited by 56
  17. 2022Neighborhood-aware Scalable Temporal Network Representation Learningcited by 54
  18. 2014Community Detection in Networks with Node Attributescited by 44
  19. 2020Neural Ordinary Differential Equation Control of Dynamics on Graphscited by 24
  20. 2023Dynamic Graph Representation Learning with Neural Networks: A Surveycited by 15
  21. 2022Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A Surveycited by 14
  22. 2023Signed Graph Neural Ordinary Differential Equation for Modeling Continuous-time Dynamicscited by 8
  23. 2022Higher-order Knowledge Transfer for Dynamic Community Detection with Great Changescited by 5
  24. 2024AutoSGNN: Automatic Propagation Mechanism Discovery for Spectral Graph Neural Networkscited by 5

Built on by

Not cited yet.