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 neighbourhood 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.
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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.
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Jhee, J.H., Yeon, J., Kwak, Y., Shin, H. (2024). Multi-scale Heat Kernel Graph Network for Graph Classification. In: Nicosia, G., Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P.M., Umeton, R. (eds) Machine Learning, Optimization, and Data Science. LOD 2023. Lecture Notes in Computer Science, vol 14506. Springer, Cham. https://doi.org/10.1007/978-3-031-53966-4_20
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