Self-/Unsupervised GNN Training

▬▬ Papers/Sources ▬▬▬▬▬▬▬
Molecular Pre-Training Evaluation: https://arxiv.org/pdf/2207.06010.pdf
Latent Space Image: https://arxiv.org/pdf/2206.08005.pdf
Survey Xie et al.: https://arxiv.org/pdf/2102.10757.pdf
Survey Liu et al.: https://arxiv.org/pdf/2103.00111.pdf
Graph Autoencoder, Kipf/Welling: https://arxiv.org/pdf/1611.07308.pdf
GraphCL: https://arxiv.org/pdf/2010.13902.pdf
Deep Graph Infomax: https://arxiv.org/pdf/1809.10341.pdf
InfoGraph: https://openreview.net/pdf?id=r1lfF2NYvH

▬▬ Used Icons ▬▬▬▬▬▬▬▬▬▬
All Icons are from flaticon: https://www.flaticon.com/authors/freepik

▬▬ Used Music ▬▬▬▬▬▬▬▬▬▬▬
Music from Uppbeat (free for Creators!):
https://uppbeat.io/t/mountaineer/autu...
License code: EHK2BNGUHRZX1CXK

▬▬ Timestamps ▬▬▬▬▬▬▬▬▬▬▬
00:00 Introduction
00:20 Applications
01:48 Unsupervised Learning on Graphs
02:28 Self-Supervised Learning
03:19 Overview SSL on Graphs
04:44 Graph/Node-level
05:16 Reconstruction Approaches
07:10 Predictive / Task Generation Approaches
08:12 Contrastive Approaches
10:51 Comments on Similarity
11:30 Final Remarks

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