Path Neural Networks: Expressive and Accurate Graph Neural Networks
Gaspard Michel Giannis Nikolentzos Johannes Lutzeyer Michalis Vazirgiannis
Graph neural networks (GNNs) have recently become the standard approach for
learning with graph-structured data. Prior work has shed light into their
potential, but also their limitations. Unfortunately, it was shown that
standard GNNs are limited in their expressive power. These models are no more
powerful than the 1-dimensional Weisfeiler-Leman (1-WL) algorithm in terms of
distinguishing non-isomorphic graphs. In this paper, we propose Path Neural
Networks (PathNNs), a model that updates node representations by aggregating
paths emanating from nodes. We derive three different variants of the PathNN
model that aggregate single shortest paths, all shortest paths and all simple
paths of length up to K. We prove that two of these variants are strictly more
powerful than the 1-WL algorithm, and we experimentally validate our
theoretical results. We find that PathNNs can distinguish pairs of
non-isomorphic graphs that are indistinguishable by 1-WL, while our most
expressive PathNN variant can even distinguish between 3-WL indistinguishable
graphs. The different PathNN variants are also evaluated on graph
classification and graph regression datasets, where in most cases, they
outperform the baseline methods.