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Found 6 papers, 6 papers with code
A Comprehensive Survey on Transfer Learning
In order to show the performance of different transfer learning models, over twenty representative transfer learning models are used for experiments.
Transfer Learning Toolkit: Primers and Benchmarks
The transfer learning toolkit wraps the codes of 17 transfer learning models and provides integrated interfaces, allowing users to use those models by calling a simple function.
Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study
In view of those, we present the first fair and reproducible benchmark dedicated to assessing the "tricks" of training deep GNNs.
A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking
Large-scale graph training is a notoriously challenging problem for graph neural networks (GNNs).
Ranked #2 on
Node Property Prediction
on ogbn-products
Contrastive Knowledge Graph Error Detection
To this end, we propose a novel framework - ContrAstive knowledge Graph Error Detection (CAGED).
SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning
More recently, with the rapid development of language models (LMs), researchers have focused on leveraging LMs to facilitate the learning of TGs, either by jointly training them in a computationally intensive framework (merging the two stages), or designing complex self-supervised training tasks for feature extraction (enhancing the first stage).
Ranked #1 on
Node Property Prediction
on ogbn-arxiv
