PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. Whether you are a machine ...

PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. Whether you are a machine ...

Notably, some geometric intersection graphs, even in the absence of small complete subgraphs (for example, triangle-free graphs), demonstrably require a high chromatic number, ...

The expressive power of Graph Neural Networks (GNNs) has been studied extensively through the Weisfeiler-Leman (WL) graph isomorphism test. However, standard GNNs and the WL framework are inapplicable ...

In experiments with geometric graphs obtained from placed benchmark VLSI circuits, our heuristic generates balanced partitions with imbalance no greater than 2%, very short runtimes, and good cutsizes ...

In this paper, we study the generalization capabilities of geometric graph neural networks (GNNs). We consider GNNs over a geometric graph constructed from a finite set of randomly sampled points over ...