WebJul 15, 2024 · We propose Graph Contrastive Learning (GraphCL), a general framework for learning node representations in a self supervised manner. GraphCL learns node embeddings by maximizing the similarity... Web受最近视觉表示学习中对比学习发展的推动（见第 2 节），我们提出了一个图对比学习框架（GraphCL）用于（自监督）GNN 预训练。 在图对比学习中，预训练是通过潜在空间中的对比损失最大化 同一图的两个增强视图之间的一致性 来执行的，如图 1 所示。

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WebIn this paper, we propose a graph contrastive learning (GraphCL) framework for learning unsupervised representations of graph data. We first design four types of graph augmentations to incorporate various priors. We then systematically study the impact of various combinations of graph augmentations on multiple datasets, in four different ... WebGITHUB Social Networks 4999 508.52 594.87 IMDB-B Social Networks 1000 19.77 96.53 MNIST Superpixel Graphs 70000 70.57 8 ... rigorously showing that GraphCL can be … philips led reflektor gu5.3 7w 50w

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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. 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 … 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. truth tarot video