A Quantum Spatial Graph Convolutional Neural Network using Quantum Passing Information

by   Lu Bai, et al.

In this paper, we develop a new Quantum Spatial Graph Convolutional Neural Network (QSGCNN) model that can directly learn a classification function for graphs of arbitrary sizes. Unlike state-of-the-art Graph Convolutional Neural Network (GCN) models, the proposed QSGCNN model incorporates the process of identifying transitive aligned vertices between graphs, and transforms arbitrary sized graphs into fixed-sized aligned vertex grid structures. To further learn representative graph characteristics, a new quantum spatial graph convolution is proposed and employed to extract multi-scale vertex features, in terms of quantum passing information between grid vertices of each graph. Since the quantum spatial convolution preserves the property of the input grid structures, the proposed QSGCNN model allows to directly employ the traditional convolutional neural network to further learn from the global graph topology, providing an end-to-end deep learning architecture that integrates the graph representation and learning in the quantum spatial graph convolution layer and the traditional convolutional layer for graph classifications. We demonstrate the effectiveness of the proposed QSGCNN model in terms of the theoretical connections to state-of-the-art methods. The proposed QSGCNN model addresses the shortcomings of information loss and imprecise information representation arising in existing GCN models associated with SortPooling or SumPooling layers. Experimental results on benchmark graph classification datasets demonstrate the effectiveness of the proposed QSGCNN model.


Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification

In this paper, we develop a novel Aligned-Spatial Graph Convolutional Ne...

Learning Vertex Convolutional Networks for Graph Classification

In this paper, we develop a new aligned vertex convolutional network mod...

DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model

Convolutional neural networks (CNNs) can be applied to graph similarity ...

TabularNet: A Neural Network Architecture for Understanding Semantic Structures of Tabular Data

Tabular data are ubiquitous for the widespread applications of tables an...

PK-GCN: Prior Knowledge Assisted Image Classification using Graph Convolution Networks

Deep learning has gained great success in various classification tasks. ...

HAQJSK: Hierarchical-Aligned Quantum Jensen-Shannon Kernels for Graph Classification

In this work, we propose a family of novel quantum kernels, namely the H...

AERK: Aligned Entropic Reproducing Kernels through Continuous-time Quantum Walks

In this work, we develop an Aligned Entropic Reproducing Kernel (AERK) f...

Please sign up or login with your details

Forgot password? Click here to reset