Prototype-Enhanced Hypergraph Learning for Heterogeneous Information Networks

• URL: http://arxiv.org/abs/2309.13092v1
• Date: Fri, 22 Sep 2023 09:51:15 GMT
• Title: Prototype-Enhanced Hypergraph Learning for Heterogeneous Information Networks
• Authors: Shuai Wang, Jiayi Shen, Athanasios Efthymiou, Stevan Rudinac, Monika Kackovic, Nachoem Wijnberg, Marcel Worring
• Abstract summary: We introduce a novel prototype-enhanced hypergraph learning approach for node classification in Heterogeneous Information Networks. Our method captures higher-order relationships among nodes and extracts semantic information without relying on metapaths.
• Score: 22.564818600608838
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The variety and complexity of relations in multimedia data lead to Heterogeneous Information Networks (HINs). Capturing the semantics from such networks requires approaches capable of utilizing the full richness of the HINs. Existing methods for modeling HINs employ techniques originally designed for graph neural networks, and HINs decomposition analysis, like using manually predefined metapaths. In this paper, we introduce a novel prototype-enhanced hypergraph learning approach for node classification in HINs. Using hypergraphs instead of graphs, our method captures higher-order relationships among nodes and extracts semantic information without relying on metapaths. Our method leverages the power of prototypes to improve the robustness of the hypergraph learning process and creates the potential to provide human-interpretable insights into the underlying network structure. Extensive experiments on three real-world HINs demonstrate the effectiveness of our method.

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