Random Features Strengthen Graph Neural Networks
This addresses a fundamental limitation in graph learning for researchers and practitioners, offering a simple enhancement to boost GNN performance on complex tasks.
The paper tackles the limited expressive power of graph neural networks (GNNs) by showing that adding random features to nodes enables GNNs to learn almost optimal polynomial-time approximation algorithms for problems like minimum dominating set and maximum matching, with experiments demonstrating improved problem-solving capabilities over standard GNNs.
Graph neural networks (GNNs) are powerful machine learning models for various graph learning tasks. Recently, the limitations of the expressive power of various GNN models have been revealed. For example, GNNs cannot distinguish some non-isomorphic graphs and they cannot learn efficient graph algorithms. In this paper, we demonstrate that GNNs become powerful just by adding a random feature to each node. We prove that the random features enable GNNs to learn almost optimal polynomial-time approximation algorithms for the minimum dominating set problem and maximum matching problem in terms of approximation ratios. The main advantage of our method is that it can be combined with off-the-shelf GNN models with slight modifications. Through experiments, we show that the addition of random features enables GNNs to solve various problems that normal GNNs, including the graph convolutional networks (GCNs) and graph isomorphism networks (GINs), cannot solve.