Learning Backtrackless Aligned-Spatial Graph Convolutional Networks for Graph Classification

no code implementations • 6 Apr 2019 • Lu Bail, Lixin Cui, Yuhang Jiao, Luca Rossi, Edwin R. Hancock

In this paper, we develop a novel Backtrackless Aligned-Spatial Graph Convolutional Network (BASGCN) model to learn effective features for graph classification.

General Classification Graph Classification

Learning Vertex Convolutional Networks for Graph Classification

no code implementations • 26 Feb 2019 • Lu Bai, Lixin Cui, Shu Wu, Yuhang Jiao, Edwin R. Hancock

In this paper, we develop a new aligned vertex convolutional network model to learn multi-scale local-level vertex features for graph classification.

General Classification Graph Classification

Graph Convolutional Neural Networks based on Quantum Vertex Saliency

no code implementations • 4 Sep 2018 • Lu Bai, Yuhang Jiao, Luca Rossi, Lixin Cui, Jian Cheng, Edwin R. Hancock

This paper proposes a new Quantum Spatial Graph Convolutional Neural Network (QSGCNN) model that can directly learn a classification function for graphs of arbitrary sizes.

General Classification Graph Classification