Abstract
Recently, graph neural networks (GNNs) have been improved under the influence of various deep learning techniques, such as attention, autoencoders, and recurrent networks. However, real-world graphs may have multiple types of vertices and edges, such as graphs of social networks, citation networks, and e-commerce data. In these cases, most GNNs that consider a homogeneous graph as input data are not suitable because they ignore the heterogeneity. Meta-path-based methods have been researched to capture both heterogeneity and structural information of heterogeneous graphs. As a meta-path is a type of graph pattern, we extend the use of meta-paths to exploit graph patterns. In this study, we propose TP-HAN, a heterogeneous graph attention network for exploiting triangle patterns. In the experiments using DBLP and IMDB, we show that TP-HAN outperforms the state-of-the-art heterogeneous graph attention network.
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Acknowledgement
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2021-2020-0-01795) supervised by the IITP (Institute of Information & Communications Technology Planning & Evaluation).
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Yi, E., Kim, MS. (2022). Exploiting Triangle Patterns for Heterogeneous Graph Attention Network. In: Bakaev, M., Ko, IY., Mrissa, M., Pautasso, C., Srivastava, A. (eds) ICWE 2021 Workshops. ICWE 2021. Communications in Computer and Information Science, vol 1508. Springer, Cham. https://doi.org/10.1007/978-3-030-92231-3_7
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DOI: https://doi.org/10.1007/978-3-030-92231-3_7
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