
Random Features Strengthen Graph Neural Networks
Graph neural networks (GNNs) are powerful machine learning models for va...
read it

The expressive power of kthorder invariant graph networks
The expressive power of graph neural network formalisms is commonly meas...
read it

The Surprising Power of Graph Neural Networks with Random Node Initialization
Graph neural networks (GNNs) are effective models for representation lea...
read it

Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
While graph kernels (GKs) are easy to train and enjoy provable theoretic...
read it

Stealing Links from Graph Neural Networks
Graph data, such as social networks and chemical networks, contains a we...
read it

A Practical Guide to Graph Neural Networks
Graph neural networks (GNNs) have recently grown in popularity in the fi...
read it

How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks
We study how neural networks trained by gradient descent extrapolate, i....
read it
A Survey on The Expressive Power of Graph Neural Networks
Graph neural networks (GNNs) are effective machine learning models for various graph learning problems. Despite their empirical successes, the theoretical limitations of GNNs have been revealed recently. Consequently, many GNN models have been proposed to overcome these limitations. In this survey, we provide a comprehensive overview of the expressive power of GNNs and provably powerful variants of GNNs.
READ FULL TEXT
Comments
There are no comments yet.