Quantum-based subgraph convolutional neural networks

Highlights：

• Depth-based subgraph convolution operation: The depth-based subgraph convolution operation scans a ‘tree’ of parameters deriving from the quantum walks on graph, which extracts local features analogous to the standard convolution operation on grid data. These local features can potentially be composed to form multi-scale structures.

• Depth-based subgraph pooling operation: our depth-based subgraph pooling operation acts on the output of the preceding layer directly without any preprocessing scheme such as clustering.

• Experiments on eight graph-structured datasets demonstrate that our model QS-CNNs are able to outperform fourteen state-of-the-art methods at the tasks of node classification and graph classification.

摘要

•We propose a new graph convolutional neural architecture based on a depth-based representation of graph structure which integrates both the global topological and local connectivity structures within a graph.•Depth-based subgraph convolution operation: The depth-based subgraph convolution operation scans a ‘tree’ of parameters deriving from the quantum walks on graph, which extracts local features analogous to the standard convolution operation on grid data. These local features can potentially be composed to form multi-scale structures.•Depth-based subgraph pooling operation: our depth-based subgraph pooling operation acts on the output of the preceding layer directly without any preprocessing scheme such as clustering.•Experiments on eight graph-structured datasets demonstrate that our model QS-CNNs are able to outperform fourteen state-of-the-art methods at the tasks of node classification and graph classification.