Abstract
Graph convolutional networks (GCNs) are powerful models for graph-structured data learning task. However, most existing GCNs may confront with two major challenges when dealing with heterogeneous graph: (1) Predefined meta-paths are required to capture the semantic relations between nodes from different types, which may not exploit all the useful information in the graph; (2) Performance degradation and semantic confusion may happen with the growth of the network depth, which limits their ability to capture long-range dependencies. To meet these challenges, we propose Dense-HGCN, an end-to-end dense connected heterogeneous convolutional neural network to learn node representation. Dense-HGCN computes the attention weights between different nodes and incorporates the information of previous layers into each layer’s aggregation process via a specific fuse function. Moreover, Dense-HGCN leverages multi-scale information for node classification or other downstream tasks. Experimental results on real-world datasets demonstrate the superior performance of Dense-HGCN in enhancing the representational power compared with several state-of-the-art methods.
The work described in this paper was supported partially by the National Natural Science Foundation of China (12271111), Special Support Plan for High Level Talents of Guangdong Province (2019TQ05X571), Foundation of Guangdong Educational Committee (2019KZDZX1023), Project of Guangdong Province Innovative Team (2020WCXTD011).
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Yan, R., Cai, J. (2024). An End-to-End Dense Connected Heterogeneous Graph Convolutional Neural Network. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14447. Springer, Singapore. https://doi.org/10.1007/978-981-99-8079-6_36
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DOI: https://doi.org/10.1007/978-981-99-8079-6_36
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