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Graph Neural Networks are notorious for its memory consumption. A recentTransformer based GNN called Graph Transformer are shown to obtain superiorperformances when long range dependencies exist. However, combining graph dataand Transformer architecture led to a combinationally worse memory issue. Wepropose a novel version of "edge regularization technique" that alleviates theneed for Positional Encoding and ultimately alleviate GT's out of memory issue.We observe that it is not clear whether having an edge regularization on top ofpositional encoding is helpful. However, it seems evident when no positionalencoding is applied, edge regularization technique indeed stably improves GT'sperformance.

Stronger Graph Transformer with Regularized Attention Scores