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Evaluating Modules in Graph Contrastive Learning
[article]
2022
arXiv
pre-print
The recent emergence of contrastive learning approaches facilitates the application on graph representation learning (GRL), introducing graph contrastive learning (GCL) into the literature. These methods contrast semantically similar and dissimilar sample pairs to encode the semantics into node or graph embeddings. However, most existing works only performed model-level evaluation, and did not explore the combination space of modules for more comprehensive and systematic studies. For effective
arXiv:2106.08171v2
fatcat:t3ruixdbazepndw2jk4cyl3yde