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About Graph Neural Networks: Self-Supervised Learning
This document summarizes recent developments in applying self-supervised learning to graph neural networks. It categorizes self-supervised learning methods for GNNs based on the training strategies and types of data used to construct pretext tasks. Several early works on designing pretext tasks for GNNs are discussed, focusing on encoding node properties and attributes. Common training strategies like pre-training and joint training are mentioned. Loss functions used in self-supervised GNN training are also briefly covered.
Detailed Information
| Author: | ['IndritNallbani'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 15 |
| Language: | English |
| Format: | |
| Price: | FREE |
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