Dynamic Node Classification

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Most implemented papers archive 2025-07-28

5 shown of 5 papers with code (10 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Towards Better Dynamic Graph Learning: New Architecture and Unified Library
  23 Mar 2023 2 repositories listed Syntology ran 1 of 3 samples · 2 unverified
  We propose DyGFormer, a new Transformer-based architecture for dynamic graph learning.
• DyG2Vec: Efficient Representation Learning for Dynamic Graphs
  30 Oct 2022 2 repositories listed
  Temporal graph neural networks have shown promising results in learning inductive representations by automatically extracting temporal patterns.
• Enhancing the Expressivity of Temporal Graph Networks through Source-Target Identification
  6 Nov 2024 1 repository listed Syntology ran 1 of 2 samples · 1 unverified · 2 pointer-only (licence)
  Despite the successful application of Temporal Graph Networks (TGNs) for tasks such as dynamic node classification and link prediction, they still perform poorly on the task of dynamic node affinity prediction -- where…
• Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs
  18 May 2023 1 repository listed
  We approach the problem by our proposed STEP, a self-supervised temporal pruning framework that learns to remove potentially redundant edges from input dynamic graphs.
• EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning
  22 Mar 2023 1 repository listed Syntology ran 0 of 8 samples · 8 unverified
  Dynamic graphs arise in various real-world applications, and it is often welcomed to model the dynamics directly in continuous time domain for its flexibility.

Syntology lines on 3 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.