LinkDist distils self-knowledge from links into a Multi-Layer Perceptron (MLP) without the need to aggregate messages.

Experiments with 8 real-world datasets show the Learnt MLP (named LinkDistMLP) can predict the label of a node without knowing its adjacencies but achieve comparable accuracy against GNNs in the contexts of both semi- and full-supervised node classification tasks.

We also introduce Contrastive Learning techniques to further boost the accuracy of LinkDist and LinkDistMLP (as CoLinkDist and CoLinkDistMLP).

中文概要

Install dependencies torch and DGL:

pip3 install torch ogb

Run the script with arguments algorithm_name, dataset_name, and dataset_split.

• Available algorithms: mlp, gcn, gcn2mlp, linkdistmlp, linkdist, colinkdsitmlp, colinkdist.
  • If the algorithm_name ends with -trans, the experiment is run with the transductive setting. Otherwise, it is run with inductive setting.
• Available datasets: cora / citeseer / pubmed / corafull / amazon-photo / amazon-com / coauthor-cs / coauthor-phy
  • We divide the dataset into a training set with dataset_split out of ten nodes, a validation set and a testing set. If dataset_split is 0, we select at most 20 nodes from every class to construct the training set.

The following command experiments GCN on Cora dataset with the inductive setting:

python3 main.py gcn cora 0

The following command experiments CoLinkDistMLP on Amazon Photo dataset with the transductive setting, the Amazon Photo dataset is divided into a training set of 60% nodes, a validation set of 20% nodes, and a testing set of 20% nodes:

python3 main.py colinkdistmlp-trans amazon-photo 6

To reproduce all experiments in our paper, run

bash run

The output logs will be saved into trans.log, induc.log, and full.log.

@article{2106.08541v1,
  author = {Yi Luo and Aiguo Chen and Ke Yan and Ling Tian},
  eprint = {2106.08541v1},
  month = {Jun},
  title = {Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes
Without Passing Messages},
  type = {article},
  url = {https://arxiv.org/abs/2106.08541v1},
  year = {2021},
}