Self-supervised learning and pre-training strategieshave developed over the
last few years especiallyfor Convolutional Neural Networks (CNNs). Re-cently
application of such methods can also be no-ticed for Graph Neural Networks
(GNNs) . In thispaper, we have used a graph based self-supervisedlearning
strategy with different loss functions (Bar-low Twins[Zbontaret al., 2021],
HSIC[Tsaiet al.,2021], VICReg[Bardeset al., 2021]) which haveshown promising
results when applied with CNNspreviously. We have also proposed a hybrid
lossfunction combining the advantages of VICReg andHSIC and called it as
VICRegHSIC. The perfor-mance of these aforementioned methods have beencompared
when applied to 7 different datasets suchas MUTAG, PROTEINS, IMDB-Binary, etc.
Ex-periments showed that our hybrid loss function per-formed better than the
remaining ones in 4 out of7 cases. Moreover, the impact of different
batchsizes, projector dimensions and data augmentationstrategies have also been
explored.

本文讨论了在图神经网络方面的自监督学习和预训练策略，使用几种不同的损失函数（Bar-low Twins、HSIC、VICReg）进行了实验，其中比较了这些方法在 7 个数据集上的表现，最终提出的 VICRegHSIC 混合损失函数优于其他方法，同时探讨了各种批量大小、投影仪尺寸和数据增强策略的影响。