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Graph Classification Based on Structural Features of Significant Nodes and Spatial Convolutional Neural Networks by Tinghuai Ma; Hongmei Wang; Lejun Zhang; Yuan Tian; Najla Al-Nabhan is a Computer Science article available to read on EtoBox.

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Many real-world problems can be abstracted into graph classification problems. Recently, graph convolutional networks have achieved great success in the task of node classification and link prediction. However, when using graph convolution network to process the task of graph classification, either global topology information or local information is ignored. Therefore, designing graph convolutional networks to improve the accuracy of graph classification has attracted more and more attention. Inspired by the use of convolutional neural networks to process graph-structured data, we put forward a new spatial convolutional neural network architecture for graph classification. To be specific, we first design a comprehensive weighting method to measure the significance of vertices in the graph based on multiple indicators to choose the central node sequence. Then, the normalization process of the graph is realized by constructing the same size neighborhood graphs for the central vertices. After that, the structural characteristics of the graph are extracted from both local and global aspects. Finally, the tensors obtained after the above steps are respectively input into the following t

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Author

Tinghuai Ma; Hongmei Wang; Lejun Zhang; Yuan Tian; Najla Al-Nabhan

Publisher

Elsevier BV

Published

2021

Language

EN

Field

Computer Science (Physical Sciences)