Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks

arXiv:2305.10544v2 Announce Type: replace
Abstract: We introduce Graph-Induced Sum-Product Networks (GSPNs), a new probabilistic framework for graph representation learning that can tractably answer probabilistic queries. Inspired by the computational trees induced by vertices in the context of message-passing neural networks, we build hierarchies of sum-product networks (SPNs) where the parameters of a parent SPN are learnable transformations of the a-posterior mixing probabilities of its children's sum units. Due to weight sharing and the tree-shaped computation graphs of GSPNs, we obtain …

abstract arxiv build computational context cs.ai cs.lg framework graph graph representation networks neural networks parameters product queries representation representation learning tractable trees type