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Found 3 papers, 1 papers with code
Constraint-Free Structure Learning with Smooth Acyclic Orientations
The structure learning problem consists of fitting data generated by a Directed Acyclic Graph (DAG) to correctly reconstruct its arcs.
Generalizing Downsampling from Regular Data to Graphs
Downsampling produces coarsened, multi-resolution representations of data and it is used, for example, to produce lossy compression and visualization of large images, reduce computational costs, and boost deep neural representation learning.
K-plex Cover Pooling for Graph Neural Networks
We introduce a novel pooling technique which borrows from classical results in graph theory that is non-parametric and generalizes well to graphs of different nature and connectivity pattern.
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