Post #483

Bag of Tricks for Semi-Supervised classification There is a nice short paper on tricks employed on improving performance of GNN. The author, Yangkun Wang, from DGL team has a lot of high scoring entries in the OGB leaderboard, so it's worth employing these tricks: they boost performance a bit but do it consistently. The tricks include: * data augmentation * using labels as node features * renormalization of adjacency matrix * novel loss functions * residual connections from the input