Learning Parametrised Graph Shift Operators. (arXiv:2101.10050v3 [cs.LG] UPDATED)

Web: http://arxiv.org/abs/2101.10050

In many domains data is currently represented as graphs and therefore, the
graph representation of this data becomes increasingly important in machine
learning. Network data is, implicitly or explicitly, always represented using a
graph shift operator (GSO) with the most common choices being the adjacency,
Laplacian matrices and their normalisations. In this paper, a novel
parametrised GSO (PGSO) is proposed, where specific parameter values result in
the most commonly used GSOs and message-passing operators in graph neural
network (GNN) frameworks. …

arxiv graph learning operators