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Found 3 papers, 2 papers with code
PlanE: Representation Learning over Planar Graphs
Graph neural networks are prominent models for representation learning over graphs, where the idea is to iteratively compute representations of nodes of an input graph through a series of transformations in such a way that the learned graph function is isomorphism invariant on graphs, which makes the learned representations graph invariants.
Shortest Path Networks for Graph Property Prediction
Most graph neural network models rely on a particular message passing paradigm, where the idea is to iteratively propagate node representations of a graph to each node in the direct neighborhood.
On the Approximability of Weighted Model Integration on DNF Structures
Weighted model counting (WMC) consists of computing the weighted sum of all satisfying assignments of a propositional formula.
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