Results 101 to 110 of about 2,491 (218)
Equivariant Hypergraph Neural Networks
, 2022 Many problems in computer vision and machine learning can be cast as learning on hypergraphs that represent higher-order relations. Recent approaches for hypergraph learning extend graph neural networks based on message passing, which is simple yet ...
Cho, Sungjun +3 more
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IET Communications, Volume 20, Issue 1, January/December 2026.This paper proposes a quality‐based incentive mechanism for federated learning (FL) in Internet of Things (IoT) systems using a Stackelberg game framework. The mechanism rewards clients based on the quality of their uploaded models, ensuring fairness and motivating higher‐quality contributions.
Qinchi Li +5 more
wiley +1 more source
Hypergraph Contrastive Collaborative Filtering
, 2022 Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing users and items into latent representation space, with their correlative patterns from interaction data.
Xu, Yong +11 more
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AHD-SLE: Anomalous Hyperedge Detection on Hypergraph Symmetric Line Expansion
AxiomsGraph anomaly detection aims to identify unusual patterns or structures in graph-structured data. Most existing research focuses on anomalous nodes in ordinary graphs with pairwise relationships.
Yingle Li +4 more
doaj +1 more source
CHGNN:A Semi-Supervised Contrastive Hypergraph Learning Network.
Hypergraphs can model higher-order relationships among data objects that are found in applications such as social networks and bioinformatics. However, recent studies on hypergraph learning that extend graph convolutional networks to hypergraphs cannot ...
Song, Yumeng +6 more
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Hypergraph spectral learning for multi-label classification
, 2008 A hypergraph is a generalization of the traditional graph in which the edges are arbitrary non-empty subsets of the vertex set. It has been applied successfully to capture high-order relations in various domains.
Liang Sun, Jieping Ye, Shuiwang Ji
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Stability and Generalization of Hypergraph Collaborative Networks
, 2023 Graph neural networks have been shown to be very effective in utilizing pairwise relationships across samples. Recently, there have been several successful proposals to generalize graph neural networks to hypergraph neural networks to exploit more ...
Yip, Andy, Wu, Hanrui, Ng, Michael
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Temporal hypergraph modeling via inter-geometrical learning [PDF]
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo termsThe student, Shivam Agarwal, accepted the attached license on 2024-04-17 at 22:18.The student, Shivam Agarwal,
Agarwal, Shivam
core
Adaptive Expansion for Hypergraph Learning
CoRRHypergraph, with its powerful ability to capture higher-order relationships, has gained significant attention recently. Consequently, many hypergraph representation learning methods have emerged to model the complex relationships among hypergraphs. In general, these methods leverage classic expansion methods to convert hypergraphs into weighted or ...
Tianyi Ma +4 more
openaire +2 more sources
Geometric Hypergraph Learning for Visual Tracking
IEEE Transactions on Cybernetics, 2017 Graph based representation is widely used in visual tracking field by finding correct correspondences between target parts in consecutive frames. However, most graph based trackers consider pairwise geometric relations between local parts. They do not make full use of the target's intrinsic structure, thereby making the representation easily disturbed ...
Dawei Du +5 more
openaire +3 more sources