7 citations · 16 across the 9 of their papers we have counts for
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cs.LG2022★ 7 cited
FoSR: First-order spectral rewiring for addressing oversquashing in GNNs
Kedar Karhadkar, Pradeep Kr. Banerjee, Guido Montúfar
Graph neural networks (GNNs) are able to leverage the structure of graph data by passing messages along the edges of the graph. While this allows GNNs to learn features depending o…
cs.LG2022★ 3 cited
Oversquashing in GNNs through the lens of information contraction and graph expansion
Pradeep Kr. Banerjee, Kedar Karhadkar, Yu Guang Wang +2
The quality of signal propagation in message-passing graph neural networks (GNNs) strongly influences their expressivity as has been observed in recent works. In particular, for pr…
math.CO2022
Lattice models, differential forms, and the Yang-Baxter equation
Kedar Karhadkar
We introduce new methods to describe admissible states of the six-vertex and the eight-vertex lattice models of statistical mechanics. For the six-vertex model, we view the admissi…