We introduce a self-supervised approach for learning node and graph level
representations by contrasting structural views of graphs. We show that unlike
visual representation learning, increasing the number of views to more than two
or contrasting multi-scale encodings do not improve performance, and the best
performance is achieved by contrasting encodings from first-order neighbors and
a graph diffusion. We achieve new state-of-the-art results in self-supervised
learning on 8 out of 8 node and graph classification benchmarks under the
linear evaluation protocol. For example, on Cora (node) and Reddit-Binary
(graph) classification benchmarks, we achieve 86.8% and 84.5% accuracy, which
are 5.5% and 2.4% relative improvements over previous state-of-the-art. When
compared to supervised baselines, our approach outperforms them in 4 out of 8
benchmarks. Source code is released at: this https URL

文章介绍了通过对比不同结构视图的图表达学习来实现自监督学习，相对于多视图或多尺度编码，通过对比一阶邻居和图扩散编码能够获得最佳表现。该方法在 8 个节点和图分类基准测试中取得了新的最佳结果，在 Cora（节点）和 Reddit-Binary（图）分类基准测试中，取得了 86.8％和 84.5％的准确性，在 4 个基准测试中优于有监督的基线。