Prototype-Enhanced Hypergraph Learning for Heterogeneous Information
  Networks
Shuai Wang Jiayi Shen Athanasios Efthymiou Stevan Rudinac Monika Kackovic Nachoem Wijnberg Marcel Worring
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.