SimGRL: a simple self-supervised graph representation learning framework via triplets

SimGRL: a simple self-supervised graph representation learning framework via triplets

Abstract Recently, graph contrastive learning (GCL) has achieved remarkable performance in graph representation learning. However, existing GCL methods usually follow a dual-channel encoder network (i.e., Siamese networks), which adds to the complexity of the network architecture. Additionally, thes...

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Bibliographic Details
Main Authors: Da Huang, Fangyuan Lei, Xi Zeng
Format: Article
Language:English
Published: Springer 2023-02-01
Series:Complex & Intelligent Systems
Subjects:
Graph representation learning
Graph neural networks
Self-supervised learning
Triplet loss
Node classification
Graph classification
Online Access:https://doi.org/10.1007/s40747-023-00997-6

Internet

https://doi.org/10.1007/s40747-023-00997-6

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