TL;DR
This paper introduces a novel prototype-enhanced hypergraph learning approach for node classification in HINs using hypergraphs instead of graphs, which captures higher-order relationships among nodes and extracts semantic information without relying on metapaths.
Modern data are increasingly represented and utilized as interconnected networks, including collaboration graphs, multi-relational user–item interactions, and schema-less knowledge graphs that support retrieval-augmented generation pipelines. While graph analytics has become a key foundation for intelligent data mining...
Hypergraphs offer a natural framework for modeling networked data, where dependencies among entities are governed by higher-order interactions. While hypergraph learning methods such as hypergraph neural networks have demonstrated remarkable predictive performance, most existing approaches rely on black-box message-pas...
Complex systems are routinely measured and represented through pairwise networks, even when the underlying interactions involve more than two units at once. Recovering this latent hypergraph structure from pairwise measurements is a fundamental inverse problem, but as the space of candidate hyperedges grows exponential...
Directed graphs naturally model many real-world systems in which interactions are asymmetric, such as citation networks, web graphs, and information-flow networks. However, graph learning methods commonly rely on message passing with symmetrized graph representations or positional encodings that only partially exploit...
The Hypergraph Identity-Aware Subtree (IA Subtree) Kernel is introduced, which distinguishes uniform-regular hypergraphs by considering both neighborhood connectivity and connection density and develops two Hypergraph Neural Networks: Hypergraph Isomorphism Networks (HGIN) and Identity-Aware Hypergraph Isomorphism Netw...
Graph representation learning has emerged as a foundational discipline within modern data science, essential for modeling the complex relational systems ubiquitous in domains from social networks to bioinformatics. However, conventional approaches, particularly Graph Neural Networks (GNN), exhibit significant limitatio...
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