mlsanityData to 14 Oct 2026

DG-CoLearn: An Efficient Collaborative Learning Framework for Dynamic Graphs

arXiv 2605.31427 ↗First posted 29 May 2026cs.LG cs.DCcited by 0

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Abstract (arXiv)

Dynamic graph learning (DGL) is essential for modelling evolving graph data, but existing methods suffer from significant computational overhead due to repeated full-snapshot retraining and are not well-suited for collaborative settings with partitioned data. In realistic graph systems, cross-partition edges are unavoidable, but direct sharing of graph structure between clients may violate privacy constraints. We propose DG-CoLearn, a client-oblivious collaborative dynamic graph learning framework built on incremental graph snapshot processing, which focuses computation on graph regions affected by temporal updates while preserving historical information through temporal modelling. This incremental design is consistently applied across the entire graph processing pipeline, including a server-mediated embedding exchange mechanism to enable accurate multi-hop message passing without exposing raw cross-client structural information. Extensive experiments demonstrate that DG-CoLearn achieves up to 33.8× speedup in training time and 27.4× reduction in communication overhead, while consistently improving predictive performance on both node classification (up to 13.36% F1 improvement) and link prediction (up to 8.27% MAP improvement) tasks. These results highlight the effectiveness of DG-CoLearn in bridging efficiency, scalability, and client-to-client structural privacy in collaborative dynamic graph learning.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 38 references that are in the map. First posted on arXiv 29 May 2026.

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References · 23

  1. 2019Deep Leakage from Gradientscited by 1,021
  2. 2020Temporal Graph Networks for Deep Learning on Dynamic Graphscited by 530
  3. 2019EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphscited by 447
  4. 2019On Tiny Episodic Memories in Continual Learningcited by 307
  5. 2023Towards Better Dynamic Graph Learning: New Architecture and Unified Librarycited by 157
  6. 2021Subgraph Federated Learning with Missing Neighbor Generationcited by 132
  7. 2022ROLAND: Graph Learning Framework for Dynamic Graphscited by 132
  8. 2020Overcoming Catastrophic Forgetting in Graph Neural Networkscited by 95
  9. 2021Membership Inference Attack on Graph Neural Networkscited by 81
  10. 2018Community Interaction and Conflict on the Webcited by 74
  11. 2022FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learningcited by 43
  12. 2024Machine Unlearning Fails to Remove Data Poisoning Attackscited by 37
11 more
  1. 2024TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphscited by 36
  2. 2021FedGraph: Federated Graph Learning with Intelligent Samplingcited by 34
  3. 2021Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networkscited by 24
  4. 2022FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networkscited by 24
  5. 2024Towards Robust Graph Incremental Learning on Evolving Graphscited by 18
  6. 2024A Survey on Machine Unlearning: Techniques and New Emerged Privacy Riskscited by 15
  7. 2022FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphscited by 12
  8. 2021Efficient Scaling of Dynamic Graph Neural Networkscited by 11
  9. 2023Personalized Graph Federated Learning with Differential Privacycited by 4
  10. 2024Gradients Stand-in for Defending Deep Leakage in Federated Learningcited by 2
  11. 2025Graph-Level Label-Only Membership Inference Attack against Graph Neural Networkscited by 2

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