Add to ORKGRecent work shows that the expressive power of Graph Neural Networks (GNNs) in distinguishing non-isomorphic graphs is exactly the same as that of the Weisfeiler-Lehman (WL) graph test. In particular, they show that the WL test can be simulated by GNNs. However, those simulations involve neural networks for the 'combine' function of size polynomial or even exponential in the number of graph nodes $n$, as well as feature vectors of length linear in $n$. We present an improved simulation of the WL test on GNNs with \emph{exponentially} lower complexity. In particular, the neural network implementing the combine function in each node has only a polylogarithmic number of parameters in $n$, and the feature vectors exchanged by the nodes of GNN...
Graph Neural Networks (GNNs) have achieved great success on a node classification task. Despite the ...
Graph Neural Networks (GNNs) are a large class of relational models for graph processing. Recent the...
While many systems have been developed to train Graph Neural Networks (GNNs), efficient model infere...
Graph Neural Networks (GNNs) are a broad class of connectionist models for graph processing. Recent ...
Graph neural networks are designed to learn functions on graphs. Typically, the relevant target func...
Graph Neural Networks (GNNs) are powerful convolutional architectures that have shown remarkable per...
Graph Neural Networks (GNNs) have achieved much success on graph-structured data. In light of this, ...
Recently, many works studied the expressive power of graph neural networks (GNNs) by linking it to t...
Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-worl...
International audienceSince the Message Passing (Graph) Neural Networks (MPNNs) have a linear comple...
This paper proposes a framework to formally link a fragment of an algebraic language to a Graph Neur...
The explainability of Graph Neural Networks (GNNs) is critical to various GNN applications but remai...
While Graph Neural Networks (GNNs) have made significant strides in diverse areas, they are hindered...
In recent years, graph neural networks (GNNs) have emerged as a powerful neural architecture to lear...
Graph neural networks (GNNs) have received great attention due to their success in various graph-rel...
Graph Neural Networks (GNNs) have achieved great success on a node classification task. Despite the ...
Graph Neural Networks (GNNs) are a large class of relational models for graph processing. Recent the...
While many systems have been developed to train Graph Neural Networks (GNNs), efficient model infere...
Graph Neural Networks (GNNs) are a broad class of connectionist models for graph processing. Recent ...
Graph neural networks are designed to learn functions on graphs. Typically, the relevant target func...
Graph Neural Networks (GNNs) are powerful convolutional architectures that have shown remarkable per...
Graph Neural Networks (GNNs) have achieved much success on graph-structured data. In light of this, ...
Recently, many works studied the expressive power of graph neural networks (GNNs) by linking it to t...
Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-worl...
International audienceSince the Message Passing (Graph) Neural Networks (MPNNs) have a linear comple...
This paper proposes a framework to formally link a fragment of an algebraic language to a Graph Neur...
The explainability of Graph Neural Networks (GNNs) is critical to various GNN applications but remai...
While Graph Neural Networks (GNNs) have made significant strides in diverse areas, they are hindered...
In recent years, graph neural networks (GNNs) have emerged as a powerful neural architecture to lear...
Graph neural networks (GNNs) have received great attention due to their success in various graph-rel...
Graph Neural Networks (GNNs) have achieved great success on a node classification task. Despite the ...
Graph Neural Networks (GNNs) are a large class of relational models for graph processing. Recent the...
While many systems have been developed to train Graph Neural Networks (GNNs), efficient model infere...