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Graph neural networks: Taxonomy, advances, and trends
Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-
dimensional spaces according to specific tasks. Up to now, there have been several surveys …
dimensional spaces according to specific tasks. Up to now, there have been several surveys …
A hypergraph neural network framework for learning hyperedge-dependent node embeddings
In this work, we introduce a hypergraph representation learning framework called
Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a …
Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a …
DHCL-BR: Dual Hypergraph Contrastive Learning for Bundle Recommendation
P Zhang, Z Niu, R Ma, F Zhang - The Computer Journal, 2024 - academic.oup.com
As an extension of conventional top-K item recommendation solution, bundle
recommendation has aroused increasingly attention. However, because of the extreme …
recommendation has aroused increasingly attention. However, because of the extreme …
Ambiguities in neural-network-based hyperedge prediction
A hypergraph is a generalization of a graph that depicts higher-order relations. Predicting
higher-order relations, ie hyperedges, is a fundamental problem in hypergraph studies, and …
higher-order relations, ie hyperedges, is a fundamental problem in hypergraph studies, and …
Multimodal feature fusion based hypergraph learning model
Z Yang, L Xu, L Zhao - Computational Intelligence and …, 2022 - Wiley Online Library
Hypergraph learning is a new research hotspot in the machine learning field. The
performance of the hypergraph learning model depends on the quality of the hypergraph …
performance of the hypergraph learning model depends on the quality of the hypergraph …