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Ali Kemal Sinop

2 accepted papers

2023

Affinity-Aware Graph Networks

NeurIPS 2023poster

Graph Neural Networks (GNNs) have emerged as a powerful technique for learning on relational data. Owing to the relatively limited number of message passing steps they perform—and hence a smaller receptive field—there has been significant interest in improving their expressivity by incorporating str…

Cited by 22SourcePDFScholar
2023

Exphormer: Sparse Transformers for Graphs

ICML 2023poster

Graph transformers have emerged as a promising architecture for a variety of graph learning and representation tasks. Despite their successes, though, it remains challenging to scale graph transformers to large graphs while maintaining accuracy competitive with message-passing networks. In this pape…