WebSelf-supervised learning on graph-structured data has drawn recent interest for learning generalizable, transferable and robust representations from unlabeled graphs. Among many, graph contrastive learning (GraphCL) has emerged with … WebExtensive experiments demonstrate that JOAO performs on par with or sometimes better than the state-of-the-art competitors including GraphCL, on multiple graph datasets of various scales and types, yet without resorting to any laborious dataset-specific tuning on augmentation selection.

[2010.13902] Graph Contrastive Learning with Augmentations - arXiv.org

http://proceedings.mlr.press/v139/you21a/you21a.pdf WebOct 22, 2024 · Unlike what has been developed for convolutional neural networks (CNNs) for image data, self-supervised learning and pre-training are less explored for GNNs. In this paper, we propose a graph... how far is new orleans from st louis mo

Graph Contrastive Learning with Augmentations (Appendix)

WebUnlike what has been developed for convolutional neural networks (CNNs) for image data, self-supervised learning and pre-training are less explored for GNNs. In this paper, we propose a graph contrastive learning (GraphCL) framework for learning unsupervised representations of graph data. WebSep 30, 2024 · Since GraphQL and Go are both statically-typed languages, we wanted to be able to write a query and automatically validate the query against our schema, then generate a Go struct which we can use in our code. And we knew it was possible: we already do similar things in our GraphQL servers and in JavaScript! A quick tour of genqlient WebJan 1, 2024 · Our principled and automated approach has proven to be competitive against the state-of-the-art graph self-supervision methods, including GraphCL, on benchmarks of small graphs; and shown even better generalizability on large-scale graphs, without resorting to human expertise or downstream validation. high boy sprayer for sale