Graph contrastive learning (GCL), as a popular approach to graph
self-supervised learning, has recently achieved a non-negligible effect. To
achieve superior performance, the majority of existing GCL methods elaborate on
graph data augmentation to construct appropriate contrastive pairs. However,
existing methods place more emphasis on the complex graph data augmentation
which requires extra time overhead, and pay less attention to developing
contrastive schemes specific to encoder characteristics. We argue that a better
contrastive scheme should be tailored to the characteristics of graph neural
networks (e.g., neighborhood aggregation) and propose a simple yet effective
method named FastGCL. Specifically, by constructing weighted-aggregated and
non-aggregated neighborhood information as positive and negative samples
respectively, FastGCL identifies the potential semantic information of data
without disturbing the graph topology and node attributes, resulting in faster
training and convergence speeds. Extensive experiments have been conducted on
node classification and graph classification tasks, showing that FastGCL has
competitive classification performance and significant training speedup
compared to existing state-of-the-art methods.

本研究提出了一个简单而有效的方法，即 FastGCL，它针对图形神经网络的特征（如邻域聚合）构建加权聚合和非聚合邻域信息作为正负样本，通过识别数据的潜在语义信息来快速训练和收敛，从而在节点分类和图分类任务中具有竞争性的分类性能和显着的训练加速。