Topology-Informed Graph Transformer

Yun Young Choi, Sun Woo Park, Minho Lee, Youngho Woo
Proceedings of the Geometry-grounded Representation Learning and Generative Modeling Workshop (GRaM), PMLR 251:20-34, 2024.

Abstract

Transformers, through their self-attention mechanisms, have revolutionized performance in Natural Language Processing and Vision. Recently,there has been increasing interest in integrating Transformers with Graph Neural Networks (GNNs) to enhance analyzing geometric properties of graphs by employing global attention mechanisms. A key challenge in improving graph transformers is enhancing their ability to distinguish between isomorphic graphs, which can potentially boost their predictive performance. To address this challenge, we introduce ’Topology-Informed Graph Transformer (TIGT)’, a novel transformer enhancing both discriminative power in detecting graph isomorphisms and the overall performance of Graph Transformers. TIGT consists of four components: (1) a topological positional embedding layer using non-isomorphic universal covers based on cyclic subgraphs of graphs to ensure unique graph representation, (2) a dual-path message-passing layer to explicitly encode topological characteristics throughout the encoder layers, (3) a global attention mechanism, and (4) a graph information layer to recalibrate channel-wise graph features for improved feature representation. TIGT outperforms previous Graph Transformers in classifying synthetic dataset aimed at distinguishing isomorphism classes of graphs. Additionally, mathematical analysis and empirical evaluations highlight our model’s competitive edge over state-of-the-art Graph Transformers across various benchmark datasets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v251-choi24a, title = {Topology-Informed Graph Transformer}, author = {Choi, Yun Young and Park, Sun Woo and Lee, Minho and Woo, Youngho}, booktitle = {Proceedings of the Geometry-grounded Representation Learning and Generative Modeling Workshop (GRaM)}, pages = {20--34}, year = {2024}, editor = {Vadgama, Sharvaree and Bekkers, Erik and Pouplin, Alison and Kaba, Sekou-Oumar and Walters, Robin and Lawrence, Hannah and Emerson, Tegan and Kvinge, Henry and Tomczak, Jakub and Jegelka, Stephanie}, volume = {251}, series = {Proceedings of Machine Learning Research}, month = {29 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v251/main/assets/choi24a/choi24a.pdf}, url = {https://proceedings.mlr.press/v251/choi24a.html}, abstract = {Transformers, through their self-attention mechanisms, have revolutionized performance in Natural Language Processing and Vision. Recently,there has been increasing interest in integrating Transformers with Graph Neural Networks (GNNs) to enhance analyzing geometric properties of graphs by employing global attention mechanisms. A key challenge in improving graph transformers is enhancing their ability to distinguish between isomorphic graphs, which can potentially boost their predictive performance. To address this challenge, we introduce ’Topology-Informed Graph Transformer (TIGT)’, a novel transformer enhancing both discriminative power in detecting graph isomorphisms and the overall performance of Graph Transformers. TIGT consists of four components: (1) a topological positional embedding layer using non-isomorphic universal covers based on cyclic subgraphs of graphs to ensure unique graph representation, (2) a dual-path message-passing layer to explicitly encode topological characteristics throughout the encoder layers, (3) a global attention mechanism, and (4) a graph information layer to recalibrate channel-wise graph features for improved feature representation. TIGT outperforms previous Graph Transformers in classifying synthetic dataset aimed at distinguishing isomorphism classes of graphs. Additionally, mathematical analysis and empirical evaluations highlight our model’s competitive edge over state-of-the-art Graph Transformers across various benchmark datasets.} }
Endnote
%0 Conference Paper %T Topology-Informed Graph Transformer %A Yun Young Choi %A Sun Woo Park %A Minho Lee %A Youngho Woo %B Proceedings of the Geometry-grounded Representation Learning and Generative Modeling Workshop (GRaM) %C Proceedings of Machine Learning Research %D 2024 %E Sharvaree Vadgama %E Erik Bekkers %E Alison Pouplin %E Sekou-Oumar Kaba %E Robin Walters %E Hannah Lawrence %E Tegan Emerson %E Henry Kvinge %E Jakub Tomczak %E Stephanie Jegelka %F pmlr-v251-choi24a %I PMLR %P 20--34 %U https://proceedings.mlr.press/v251/choi24a.html %V 251 %X Transformers, through their self-attention mechanisms, have revolutionized performance in Natural Language Processing and Vision. Recently,there has been increasing interest in integrating Transformers with Graph Neural Networks (GNNs) to enhance analyzing geometric properties of graphs by employing global attention mechanisms. A key challenge in improving graph transformers is enhancing their ability to distinguish between isomorphic graphs, which can potentially boost their predictive performance. To address this challenge, we introduce ’Topology-Informed Graph Transformer (TIGT)’, a novel transformer enhancing both discriminative power in detecting graph isomorphisms and the overall performance of Graph Transformers. TIGT consists of four components: (1) a topological positional embedding layer using non-isomorphic universal covers based on cyclic subgraphs of graphs to ensure unique graph representation, (2) a dual-path message-passing layer to explicitly encode topological characteristics throughout the encoder layers, (3) a global attention mechanism, and (4) a graph information layer to recalibrate channel-wise graph features for improved feature representation. TIGT outperforms previous Graph Transformers in classifying synthetic dataset aimed at distinguishing isomorphism classes of graphs. Additionally, mathematical analysis and empirical evaluations highlight our model’s competitive edge over state-of-the-art Graph Transformers across various benchmark datasets.
APA
Choi, Y.Y., Park, S.W., Lee, M. & Woo, Y.. (2024). Topology-Informed Graph Transformer. Proceedings of the Geometry-grounded Representation Learning and Generative Modeling Workshop (GRaM), in Proceedings of Machine Learning Research 251:20-34 Available from https://proceedings.mlr.press/v251/choi24a.html.

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