Add to ORKGAccepted at ICML 2023International audienceGraph 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...
We focus on graph classification using a graph neural network (GNN) model that precomputes the node ...
Graph neural networks (GNNs) have known an increasing success recently, with many GNN variants achie...
The human brain’s reasoning is postulated to be done by the creation of graphs from the experiences ...
In recent years, graph neural networks (GNNs) have emerged as a powerful neural architecture to lear...
Most graph neural network models rely on a particular message passing paradigm, where the idea is to...
Despite the great achievements of Graph Neural Networks (GNNs) in graph learning, conventional GNNs ...
Seminal works on graph neural networks have primarily targeted semi-supervised node classification p...
While Graph Neural Networks (GNNs) have made significant strides in diverse areas, they are hindered...
A hallmark of graph neural networks is their ability to distinguish the isomorphism class of their i...
Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theore...
In recent years, graph neural networks (GNN) have succeeded in many structural data analyses, includ...
In several applications the information is naturally represented by graphs. Traditional approaches c...
Graph Neural Networks (GNNs) have shown great potential in the field of graph representation learnin...
Message passing Graph Neural Networks (GNNs) provide a powerful modeling framework for relational da...
Experimental reproducibility and replicability are critical topics in machine learning. Authors hav...
We focus on graph classification using a graph neural network (GNN) model that precomputes the node ...
Graph neural networks (GNNs) have known an increasing success recently, with many GNN variants achie...
The human brain’s reasoning is postulated to be done by the creation of graphs from the experiences ...
In recent years, graph neural networks (GNNs) have emerged as a powerful neural architecture to lear...
Most graph neural network models rely on a particular message passing paradigm, where the idea is to...
Despite the great achievements of Graph Neural Networks (GNNs) in graph learning, conventional GNNs ...
Seminal works on graph neural networks have primarily targeted semi-supervised node classification p...
While Graph Neural Networks (GNNs) have made significant strides in diverse areas, they are hindered...
A hallmark of graph neural networks is their ability to distinguish the isomorphism class of their i...
Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theore...
In recent years, graph neural networks (GNN) have succeeded in many structural data analyses, includ...
In several applications the information is naturally represented by graphs. Traditional approaches c...
Graph Neural Networks (GNNs) have shown great potential in the field of graph representation learnin...
Message passing Graph Neural Networks (GNNs) provide a powerful modeling framework for relational da...
Experimental reproducibility and replicability are critical topics in machine learning. Authors hav...
We focus on graph classification using a graph neural network (GNN) model that precomputes the node ...
Graph neural networks (GNNs) have known an increasing success recently, with many GNN variants achie...
The human brain’s reasoning is postulated to be done by the creation of graphs from the experiences ...