Dynamic Node Classification
4 papers with code • 0 benchmarks • 0 datasets
node classification on temporal graphs
Benchmarks
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Most implemented papers
DyG2Vec: Efficient Representation Learning for Dynamic Graphs
Temporal graph neural networks have shown promising results in learning inductive representations by automatically extracting temporal patterns.
EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning
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.
Towards Better Dynamic Graph Learning: New Architecture and Unified Library
yule-buaa/dyglib
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NeurIPS 2023
We propose DyGFormer, a new Transformer-based architecture for dynamic graph learning.
Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs
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.
