Dynamic graph embedding

Benchmarks archive 2025-07-28

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Datasets archive 2025-07-28

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Subtasks archive 2025-07-28

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Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

10 shown of 10 papers with code (24 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.

• Toward Structure Fairness in Dynamic Graph Embedding: A Trend-aware Dual Debiasing Approach
  19 Jun 2024 1 repository listed
  We first identify biased structural evolutions in a dynamic graph based on the evolving trend of vertex degree and then propose FairDGE, the first structurally Fair Dynamic Graph Embedding algorithm.
• DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs
  17 Jun 2024 1 repository listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  Moreover, we conduct extensive benchmark experiments on DTGB, evaluating 7 popular dynamic graph learning algorithms and their variants of adapting to text attributes with LLM embeddings, along with 6 powerful large…
• Valid Conformal Prediction for Dynamic GNNs
  29 May 2024 1 repository listed Syntology ran 6 of 12 samples · 6 unverified · 12 pointer-only (licence)
  In this work we propose to use a dynamic graph representation known in the tensor literature as the unfolding, to achieve valid prediction sets via conformal prediction.
• TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers
  5 Jul 2023 1 repository listed
  Hence, incorporating long-range dependencies from the historical graph context plays a crucial role in accurately learning their temporal dynamics.
• Parameter-free Dynamic Graph Embedding for Link Prediction
  15 Oct 2022 1 repository listed Syntology ran 3 of 5 samples · 2 unverified · 5 pointer-only (licence)
  Dynamic interaction graphs have been widely adopted to model the evolution of user-item interactions over time.
• DynG2G: An Efficient Stochastic Graph Embedding Method for Temporal Graphs
  28 Sep 2021 1 repository listed
  However, recent advances mostly focus on learning node embeddings as deterministic "vectors" for static graphs yet disregarding the key graph temporal dynamics and the evolving uncertainties associated with node…
• FeatureNorm: L2 Feature Normalization for Dynamic Graph Embedding
  27 Feb 2021 1 repository listed
  As a matter of fact, this smoothing technique can not only encourage must-link node pairs to get closer but also push cannot-link pairs to shrink together, which potentially cause serious feature shrink or oversmoothing…
• K-Core based Temporal Graph Convolutional Network for Dynamic Graphs
  22 Mar 2020 1 repository listed
  Graph representation learning is a fundamental task in various applications that strives to learn low-dimensional embeddings for nodes that can preserve graph topology information.
• FILDNE: A Framework for Incremental Learning of Dynamic Networks Embeddings
  6 Apr 2019 1 repository listed
  Experimental results on several downstream tasks, over seven real-world data sets, show that FILDNE is able to reduce memory and computational time costs while providing competitive quality measure gains with respect to…
• DynamicGEM: A Library for Dynamic Graph Embedding Methods
  26 Nov 2018 1 repository listed
  DynamicGEM is an open-source Python library for learning node representations of dynamic graphs.

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.