Add to ORKGThis paper studies asynchronous message passing (AMP), a new paradigm for applying neural network based learning to graphs. Existing graph neural networks use the synchronous distributed computing model and aggregate their neighbors in each round, which causes problems such as oversmoothing and limits their expressiveness. On the other hand, AMP is based on the asynchronous model, where nodes react to messages of their neighbors individually. We prove that (i) AMP can simulate synchronous GNNs and that (ii) AMP can theoretically distinguish any pair of graphs. We experimentally validate AMP's expressiveness. Further, we show that AMP might be better suited to propagate messages over large distances in graphs and performs well on several gra...
We propose a new Graph Neural Network that combines re-cent advancements in the field. We give theo...
In several applications the information is naturally represented by graphs. Traditional approaches c...
The notion of graph shift, introduced recently in graph signal processing, extends many classical si...
Graph neural networks (GNNs) are among the most powerful tools in deep learning. They routinely solv...
Structures or graphs are pervasive in our lives. Although deep learning has achieved tremendous succ...
The theme of this dissertation is machine learning on graph data. Graphs are generic models of signa...
Graph Neural Networks (GNNs) are powerful and flexible neural networks that use the naturally sparse...
Graph Neural Networks (GNNs) are powerful and flexible neural networks that use the naturally sparse...
Graph neural networks (GNN) have shown outstanding applications in many fields where data is fundame...
A graph is a relational data structure suitable for representing non-Euclidean structured data. In r...
Graph neural networks (GNN) have shown outstanding applications in fields where data is essentially ...
Most graph neural network models rely on a particular message passing paradigm, where the idea is to...
A hallmark of graph neural networks is their ability to distinguish the isomorphism class of their i...
International audienceSince the Message Passing (Graph) Neural Networks (MPNNs) have a linear comple...
We present Gradient Gating (G2), a novel framework for improving the performance of Graph Neural Net...
We propose a new Graph Neural Network that combines re-cent advancements in the field. We give theo...
In several applications the information is naturally represented by graphs. Traditional approaches c...
The notion of graph shift, introduced recently in graph signal processing, extends many classical si...
Graph neural networks (GNNs) are among the most powerful tools in deep learning. They routinely solv...
Structures or graphs are pervasive in our lives. Although deep learning has achieved tremendous succ...
The theme of this dissertation is machine learning on graph data. Graphs are generic models of signa...
Graph Neural Networks (GNNs) are powerful and flexible neural networks that use the naturally sparse...
Graph Neural Networks (GNNs) are powerful and flexible neural networks that use the naturally sparse...
Graph neural networks (GNN) have shown outstanding applications in many fields where data is fundame...
A graph is a relational data structure suitable for representing non-Euclidean structured data. In r...
Graph neural networks (GNN) have shown outstanding applications in fields where data is essentially ...
Most graph neural network models rely on a particular message passing paradigm, where the idea is to...
A hallmark of graph neural networks is their ability to distinguish the isomorphism class of their i...
International audienceSince the Message Passing (Graph) Neural Networks (MPNNs) have a linear comple...
We present Gradient Gating (G2), a novel framework for improving the performance of Graph Neural Net...
We propose a new Graph Neural Network that combines re-cent advancements in the field. We give theo...
In several applications the information is naturally represented by graphs. Traditional approaches c...
The notion of graph shift, introduced recently in graph signal processing, extends many classical si...