Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

2-2023

Abstract

The graph kernel of the functional brain network is an effective method in the field of neuropsychiatric disease diagnosis like Alzheimer's Disease (AD). The traditional static brain networks cannot reflect dynamic changes of brain activities, but evolving brain networks, which are a series of brain networks over time, are able to seize such dynamic changes. As far as we know, the graph kernel method is effective for calculating the differences among networks. Therefore, it has a great potential to understand the dynamic changes of evolving brain networks, which are a series of chronological differences. However, if the conventional graph kernel methods which are built for static networks are applied directly to evolving networks, the evolving information will be lost and accurate diagnostic results will be far from reach. We propose an effective method, called Global Matching based Graph Kernels (GM-GK), which captures dynamic changes of evolving brain networks and significantly improves classification accuracy. At the same time, in order to reflect the natural properties of the brain activity of the evolving brain network neglected by the GM-GK method, we also propose a Local Matching based Graph Kernel (LM-GK), which allows the order of the evolving brain network to be locally fine-tuned. Finally, the experiments are conducted on real data sets and the results show that the proposed methods can significantly improve the neuropsychiatric disease diagnostic accuracy.

Keywords

disease diagnosis, evolving brain networks, global matching, graph kernels, local matching

Discipline

Artificial Intelligence and Robotics | OS and Networks

Research Areas

Intelligent Systems and Optimization

Publication

WSDM 2023: Proceedings of the 16th ACM International Conference on Web Search and Data Mining: Singapore, February 27-March 3

First Page

150

Last Page

158

ISBN

9781450394079

Identifier

10.1145/3539597.3570449

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3539597.3570449

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