Neural sheaf diffusion: a topological perspective on heterophily and oversmoothing in GNNs
Cellular sheaves equip graphs with a “geometrical” structure by assigning vector spaces and linear maps to nodes and edges. Graph Neural Networks (GNNs) implicitly assume a graph with a trivial underlying sheaf. This choice is reflected in the structure of the graph Laplacian operator, the propertie...
| Main Authors: | , , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
Curran Associates
2023
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