research-article

Effective Graph Kernels for Evolving Functional Brain Networks

Authors:

Ge YuAuthors Info & Claims

WSDM '23: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining

Pages 150 - 158

https://doi.org/10.1145/3539597.3570449

Published: 27 February 2023 Publication History

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.

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Cited By

Xu JHe KLan MBian QLi WLi TKe YQiao MSerra ESpezzano F(2024)Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition IdentificationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679560(2671-2681)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679560
Wang XXin JWang ZQu LLi JWang Z(2024)Graph kernel of brain networks considering functional similarity measuresComputers in Biology and Medicine10.1016/j.compbiomed.2024.108148171(108148)Online publication date: Mar-2024
https://doi.org/10.1016/j.compbiomed.2024.108148

Index Terms

Effective Graph Kernels for Evolving Functional Brain Networks
1. Theory of computation
  1. Design and analysis of algorithms
    1. Graph algorithms analysis
      1. Dynamic graph algorithms

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cover image ACM Conferences

WSDM '23: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining

February 2023

1345 pages

ISBN:9781450394079

DOI:10.1145/3539597

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Hady Lauw
Singapore Management University
,
Program Chairs:
Luo Si
Salesforce
,
Evimaria Terzi
Boston University
,
Panayiotis Tsaparas
University of Ioannina

Copyright © 2023 ACM.

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Published: 27 February 2023

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February 27 - March 3, 2023

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Cited By

Xu JHe KLan MBian QLi WLi TKe YQiao MSerra ESpezzano F(2024)Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition IdentificationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679560(2671-2681)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679560
Wang XXin JWang ZQu LLi JWang Z(2024)Graph kernel of brain networks considering functional similarity measuresComputers in Biology and Medicine10.1016/j.compbiomed.2024.108148171(108148)Online publication date: Mar-2024
https://doi.org/10.1016/j.compbiomed.2024.108148

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