Add to ORKGRecent works have investigated the role of graph bottlenecks in preventing long-range information propagation in message-passing graph neural networks, causing the so-called `over-squashing' phenomenon. As a remedy, graph rewiring mechanisms have been proposed as preprocessing steps. Graph Echo State Networks (GESNs) are a reservoir computing model for graphs, where node embeddings are recursively computed by an untrained message-passing function. In this paper, we show that GESNs can achieve a significantly better accuracy on six heterophilic node classification tasks without altering the graph connectivity, thus suggesting a different route for addressing the over-squashing problem.Comment: Extended Abstract. Presented at the First Learni...
International audienceWe analyze graph smoothing with \emph{mean aggregation}, where each node succe...
Existing Graph Neural Networks (GNNs) follow the message-passing mechanism that conducts information...
Graph Neural Networks (GNNs) are powerful deep learning methods for Non-Euclidean data. Popular GNNs...
Most graph neural networks (GNNs) use the message passing paradigm, in which node features are propa...
Node classification tasks on graphs are addressed via fully-trained deep message-passing models that...
In designing and applying graph neural networks, we often fall into some optimization pitfalls, the ...
International audienceGraph Neural Networks (GNNs) have succeeded in various computer science applic...
Training deep graph neural networks (GNNs) poses a challenging task, as the performance of GNNs may ...
Over-squashing and over-smoothing are two critical issues, that limit the capabilities of graph neur...
Increasing the depth of GCN, which is expected to permit more expressivity, is shown to incur perfor...
Message-passing graph neural networks (MPNNs) emerged as powerful tools for processing graph-structu...
Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-worl...
We analyze graph smoothing with \emph{mean aggregation}, where each node successively receives the a...
Graph Neural Networks (GNNs) typically operate by message-passing, where the state of a node is upda...
Many improvements on GNNs can be deemed as operations on the spectrum of the underlying graph matrix...
International audienceWe analyze graph smoothing with \emph{mean aggregation}, where each node succe...
Existing Graph Neural Networks (GNNs) follow the message-passing mechanism that conducts information...
Graph Neural Networks (GNNs) are powerful deep learning methods for Non-Euclidean data. Popular GNNs...
Most graph neural networks (GNNs) use the message passing paradigm, in which node features are propa...
Node classification tasks on graphs are addressed via fully-trained deep message-passing models that...
In designing and applying graph neural networks, we often fall into some optimization pitfalls, the ...
International audienceGraph Neural Networks (GNNs) have succeeded in various computer science applic...
Training deep graph neural networks (GNNs) poses a challenging task, as the performance of GNNs may ...
Over-squashing and over-smoothing are two critical issues, that limit the capabilities of graph neur...
Increasing the depth of GCN, which is expected to permit more expressivity, is shown to incur perfor...
Message-passing graph neural networks (MPNNs) emerged as powerful tools for processing graph-structu...
Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-worl...
We analyze graph smoothing with \emph{mean aggregation}, where each node successively receives the a...
Graph Neural Networks (GNNs) typically operate by message-passing, where the state of a node is upda...
Many improvements on GNNs can be deemed as operations on the spectrum of the underlying graph matrix...
International audienceWe analyze graph smoothing with \emph{mean aggregation}, where each node succe...
Existing Graph Neural Networks (GNNs) follow the message-passing mechanism that conducts information...
Graph Neural Networks (GNNs) are powerful deep learning methods for Non-Euclidean data. Popular GNNs...