Abstract: Current state-of-the-art self-supervised learning methods for graph neural
networks (GNNs) are based on contrastive learning. As such, they heavily depend
on the construction of augmentations and negative examples. For example, on the
standard PPI benchmark, increasing the number of negative pairs improves
performance, thereby requiring computation and memory cost quadratic in the
number of nodes to achieve peak performance. Inspired by BYOL, a recently
introduced method for self-supervised learning that does not require negative
pairs, we present Bootstrapped Graph Latents, BGRL, a self-supervised graph
representation method that gets rid of this potentially quadratic bottleneck.
BGRL outperforms or matches the previous unsupervised state-of-the-art results
on several established benchmark datasets. Moreover, it enables the effective
usage of graph attentional (GAT) encoders, allowing us to further improve the
state of the art. In particular on the PPI dataset, using GAT as an encoder we
achieve state-of-the-art 70.49% Micro-F1, using the linear evaluation protocol.
On all other datasets under consideration, our model is competitive with the
equivalent supervised GNN results, often exceeding them.
From Spectral Graph Convolutions to Large Scale Graph Convolutional
Networks [0.0] グラフ畳み込みネットワーク(GCN)は、様々なタスクにうまく適用された強力な概念であることが示されている。