Results 21 to 30 of about 58,352 (274)
Hypergraph Neural Networks [PDF]
AAAI Conference on Artificial Intelligence, 2018 In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure.
Yifan Feng +4 more
semanticscholar +1 more source
On domination in an edge product hypergraphs [PDF]
Journal of Hyperstructures, 2021 In this paper, we study domination in an edge product hypergraphs and found some results on it. It is proved that theunit edge in a unit edge product hypergraph is a dominating set of hypergraph H.
Kishor F. Pawar, Megha M. Jadhav
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Sheaf Hypergraph Networks [PDF]
Neural Information Processing Systems, 2023 Higher-order relations are widespread in nature, with numerous phenomena involving complex interactions that extend beyond simple pairwise connections.
Iulia Duta +3 more
semanticscholar +1 more source
Unified Low-Rank Subspace Clustering with Dynamic Hypergraph for Hyperspectral Image
Remote Sensing, 2021 Low-rank representation with hypergraph regularization has achieved great success in hyperspectral imagery, which can explore global structure, and further incorporate local information.
Jinhuan Xu, Liang Xiao, Jingxiang Yang
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A Survey on Hypergraph Representation Learning
ACM Computing Surveys, 2023 Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in naturally modeling a broad range of systems where high-order relationships exist among their interacting parts.
Alessia Antelmi +5 more
semanticscholar +1 more source
The treewidth of 2-section of hypergraphs [PDF]
Discrete Mathematics & Theoretical Computer Science, 2021 Let $H=(V,F)$ be a simple hypergraph without loops. $H$ is called linear if $|f\cap g|\le 1$ for any $f,g\in F$ with $f\not=g$. The $2$-section of $H$, denoted by $[H]_2$, is a graph with $V([H]_2)=V$ and for any $ u,v\in V([H]_2)$, $uv\in E([H]_2)$ if ...
Ke Liu, Mei Lu
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Spatio-Temporal Hypergraph Learning for Next POI Recommendation
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023 Next Point-of-Interest (POI) recommendation task focuses on predicting the immediate next position a user would visit, thus providing appealing location advice.
Xiaodong Yan +6 more
semanticscholar +1 more source
Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation [PDF]
International Conference on Information and Knowledge Management, 2021 With the prevalence of social media, there has recently been a proliferation of recommenders that shift their focus from individual modeling to group recommendation. Since the group preference is a mixture of various predilections from group members, the
Junwei Zhang +5 more
semanticscholar +1 more source
Multi-site Hyper-graph Convolutional Neural Networks and Application [PDF]
Jisuanji kexue, 2022 Recently,the exploitation of graph neural networks for neurological brain disorder diagnosis has attracted much attention.However,the graphs used in the existing studies are usually based on the pairwise connections of different nodes,and thus cannot ...
ZHOU Hai-yu, ZHANG Dao-qiang
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From Hypergraph Energy Functions to Hypergraph Neural Networks [PDF]
International Conference on Machine Learning, 2023 Hypergraphs are a powerful abstraction for representing higher-order interactions between entities of interest. To exploit these relationships in making downstream predictions, a variety of hypergraph neural network architectures have recently been ...
Yuxin Wang +4 more
semanticscholar +1 more source