Self-Supervised Graph Representation Learning
Collection
Getting started with Self Supervised Graph Representation Learning. The fundamental papers, pre-processed datasets and models (soon !) • 15 items • Updated
• 1
text stringlengths 5 4.18k |
|---|
node_id node_id |
3475 2849 |
3475 3106 |
3475 808 |
3475 4555 |
3475 3563 |
3475 1527 |
3475 3327 |
402 4066 |
402 3908 |
402 2820 |
402 4903 |
402 715 |
402 5112 |
817 3563 |
817 4855 |
5057 4481 |
5057 3975 |
5057 2408 |
5057 2539 |
5057 4236 |
5057 3372 |
5057 4303 |
5057 4658 |
5057 3829 |
5057 5078 |
5057 313 |
5057 4410 |
777 3609 |
777 5122 |
777 4650 |
777 5093 |
777 4862 |
2930 169 |
2930 5067 |
2930 3700 |
2930 2238 |
2890 4544 |
2890 2371 |
2890 5123 |
2890 1481 |
2890 1039 |
2890 3668 |
2890 1654 |
2890 1883 |
2890 4796 |
2890 2173 |
2890 3644 |
4664 1316 |
4664 4965 |
4664 5194 |
4664 2219 |
4664 3630 |
4664 3603 |
4664 3253 |
4664 3413 |
4664 4989 |
4664 4862 |
2972 5074 |
2972 2958 |
3691 4419 |
3691 5125 |
3691 3909 |
3691 4903 |
3691 4346 |
3691 5196 |
3691 4365 |
3691 3852 |
3691 4847 |
3691 3730 |
3691 3891 |
3691 4434 |
3691 4730 |
3691 4758 |
3691 792 |
3691 4598 |
3691 5050 |
3691 5115 |
3691 4088 |
3691 5112 |
3691 4471 |
3310 5121 |
3310 514 |
3310 3078 |
3310 5126 |
3310 4616 |
3310 1548 |
3310 4621 |
3310 5132 |
3310 4625 |
3310 4627 |
3310 2068 |
3310 4629 |
3310 5142 |
3310 4120 |
3310 5145 |
3310 5146 |
3310 2040 |
3310 5148 |
3310 4638 |
| # Nodes | # Edges | # Features |
|---|---|---|
| 5,201 | 217,073 | 2,089 |
from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="SauravMaheshkar/pareto-squirrel", filename="processed/squirrel.bin", local_dir="./data/", repo_type="dataset")
dataset, _ = dgl.load_graphs("./data/processed/squirrel.bin")
Thank you @severo for helping me figure out the usage.
Pre-processed as per the official codebase of https://arxiv.org/abs/2210.02016
@article{ju2023multi,
title={Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization},
author={Ju, Mingxuan and Zhao, Tong and Wen, Qianlong and Yu, Wenhao and Shah, Neil and Ye, Yanfang and Zhang, Chuxu},
booktitle={International Conference on Learning Representations},
year={2023}
}
@article{DBLP:journals/corr/abs-1909-13021,
author = {Benedek Rozemberczki and
Carl Allen and
Rik Sarkar},
title = {Multi-scale Attributed Node Embedding},
journal = {CoRR},
volume = {abs/1909.13021},
year = {2019},
url = {http://arxiv.org/abs/1909.13021},
eprinttype = {arXiv},
eprint = {1909.13021},
timestamp = {Wed, 02 Oct 2019 13:04:08 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1909-13021.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}