GATSMOTE: Improving Imbalanced Node Classification on Graphs via Attention and Homophily

Yongxu Liu, Zhi Zhang, Yan Liu and Yao Zhu
Additional contact information
Yongxu Liu: Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Zhi Zhang: Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Yan Liu: Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Yao Zhu: Data Mining Center of Traditional Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing 210029, China

Mathematics, 2022, vol. 10, issue 11, 1-18

Abstract: In recent decades, non-invasive neuroimaging techniques and graph theories have enabled a better understanding of the structural patterns of the human brain at a macroscopic level. As one of the most widely used non-invasive techniques, an electroencephalogram (EEG) may collect non-neuronal signals from “bad channels”. Automatically detecting these bad channels represents an imbalanced classification task; research on the topic is rather limited. Because the human brain can be naturally modeled as a complex graph network based on its structural and functional characteristics, we seek to extend previous imbalanced node classification techniques to the bad-channel detection task. We specifically propose a novel edge generator considering the prominent small-world organization of the human brain network. We leverage the attention mechanism to adaptively calculate the weighted edge connections between each node and its neighboring nodes. Moreover, we follow the homophily assumption in graph theory to add edges between similar nodes. Adding new edges between nodes sharing identical labels shortens the path length, thus facilitating low-cost information messaging.

Keywords: class-imbalance; edge prediction; EEG; attention; node classification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/11/1799/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/11/1799/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:11:p:1799-:d:823016

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().