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cs.LG2024
Topological Blindspots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity
Yam Eitan, Yoav Gelberg, Guy Bar-Shalom +3
Topological deep learning (TDL) is a rapidly growing field that seeks to leverage topological structure in data and facilitate learning from data supported on topological objects,…
cs.LG2024
A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening
Guy Bar-Shalom, Yam Eitan, Fabrizio Frasca +1
Subgraph GNNs enhance message-passing GNNs expressivity by representing graphs as sets of subgraphs, demonstrating impressive performance across various tasks. However, their scala…
cs.LG2024
Subgraphormer: Unifying Subgraph GNNs and Graph Transformers via Graph Products
Guy Bar-Shalom, Beatrice Bevilacqua, Haggai Maron
In the realm of Graph Neural Networks (GNNs), two exciting research directions have recently emerged: Subgraph GNNs and Graph Transformers. In this paper, we propose an architectur…