Pure Transformers are Powerful Graph Learners

Jinwoo Kim · Dat Nguyen · Seonwoo Min · Sungjun Cho · Moontae Lee · Honglak Lee · Seunghoon Hong

Hall J #438

Keywords: [ Graph Transformer ] [ equivariant neural network ] [ Graph neural network ] [ graph positional embedding ] [ transformer ] [ graph ] [ self-attention ] [ permutation equivariance ]


We show that standard Transformers without graph-specific modifications can lead to promising results in graph learning both in theory and practice. Given a graph, we simply treat all nodes and edges as independent tokens, augment them with token embeddings, and feed them to a Transformer. With an appropriate choice of token embeddings, we prove that this approach is theoretically at least as expressive as an invariant graph network (2-IGN) composed of equivariant linear layers, which is already more expressive than all message-passing Graph Neural Networks (GNN). When trained on a large-scale graph dataset (PCQM4Mv2), our method coined Tokenized Graph Transformer (TokenGT) achieves significantly better results compared to GNN baselines and competitive results compared to Transformer variants with sophisticated graph-specific inductive bias. Our implementation is available at

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