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Multi-scale Heat Kernel Graph Network for Graph Classification
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Publication Year
2024-01-01
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher
Springer Science and Business Media Deutschland GmbH
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol.14506 LNCS, pp.270-282
Keyword
Graph classificationGraph convolutional networksHeat kernelLocal and global structure
Mesh Keyword
Convolutional networksGlobal structureGraph classificationGraph convolutional networkGraph neural networksHeat kernelLearn+Local informationLocal structureMulti-scales
All Science Classification Codes (ASJC)
Theoretical Computer ScienceComputer Science (all)
Abstract
Graph neural networks (GNNs) have been shown to be useful in a variety of graph classification tasks, from bioinformatics to social networks. However, most GNNs represent the graph using local neigh-bourhood aggregation. This mechanism is inherently difficult to learn about the global structure of a graph and does not have enough expressive power to distinguish simple non-isomorphic graphs. To overcome the limitation, here we propose multi-head heat kernel convolution for graph representation. Unlike the conventional approach of aggregating local information from neighbours using an adjacency matrix, the proposed method uses multiple heat kernels to learn the local information and the global structure simultaneously. The proposed algorithm outperforms the competing methods in most benchmark datasets or at least shows comparable performance.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/37109
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85186271416&origin=inward
DOI
https://doi.org/10.1007/978-3-031-53966-4_20
Journal URL
https://www.springer.com/series/558
Type
Conference
Funding
Acknowledgments. This research was supported by Institute for Information communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2022-0-00653, Voice Phishing Information Collection and Processing and Development of a Big Data Based Investigation Support System), BK21 FOUR program of the National Research Foundation of Korea funded by the Ministry of Education(NRF5199991014091), the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C2003474) and the Ajou University research fund.This research was supported by Institute for Information communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2022-0-00653, Voice Phishing Information Collection and Processing and Development of a Big Data Based Investigation Support System), BK21 FOUR program of the National Research Foundation of Korea funded by the Ministry of Edu-cation(NRF5199991014091), the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C2003474) and the Ajou University research fund.
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