Results 11 to 20 of about 5,998 (189)
Proceedings of the AAAI Conference on Artificial Intelligence, 2019 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. Confronting the challenges of learning representation for complex data in real practice, we propose to incorporate such data structure in a hypergraph, which is more flexible ...
Feng, Yifan +4 more
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Directed Hypergraph Neural Network [PDF]
Journal of Advanced Research in Dynamical and Control Systems, 2020 To deal with irregular data structure, graph convolution neural networks have been developed by a lot of data scientists. However, data scientists just have concentrated primarily on developing deep neural network method for un-directed graph. In this paper, we will present the novel neural network method for directed hypergraph. In the other words, we
Tran, Loc Hoang, Tran, Linh Hoang
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Directed hypergraph attention network for traffic forecasting
IET Intelligent Transport Systems, 2022 In traffic systems, traffic forecasting is a critical issue, which has attracted much interest from researchers. It is a challenging task due to the complex spatial‐temporal patterns of traffic data.
Xiaoyi Luo, Jiaheng Peng, Jun Liang
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Face Spoof Attack Detection with Hypergraph Capsule Convolutional Neural Networks
International Journal of Computational Intelligence Systems, 2021 Face authentication has been widely used in personal identification. However, face authentication systems can be attacked by fake images. Existing methods try to detect such attacks with different features.
Yuxin Liang, Chaoqun Hong, Weiwei Zhuang
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DeepNC: a framework for drug-target interaction prediction with graph neural networks [PDF]
PeerJ, 2022 The exploration of drug-target interactions (DTI) is an essential stage in the drug development pipeline. Thanks to the assistance of computational models, notably in the deep learning approach, scientists have been able to shorten the time spent on this
Huu Ngoc Tran Tran +2 more
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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 fundamentally limited in modeling long-range dependencies and expressive power. On the other hand, tensor-
Kim, Jinwoo +3 more
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Dynamic Hypergraph Neural Networks [PDF]
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019 In recent years, graph/hypergraph-based deep learning methods have attracted much attention from researchers. These deep learning methods take graph/hypergraph structure as prior knowledge in the model. However, hidden and important relations are not directly represented in the inherent structure.
Jianwen Jiang +4 more
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Residual Enhanced Multi-Hypergraph Neural Network [PDF]
2021 IEEE International Conference on Image Processing (ICIP), 2021 Hypergraphs are a generalized data structure of graphs to model higher-order correlations among entities, which have been successfully adopted into various research domains. Meanwhile, HyperGraph Neural Network (HGNN) is currently the de-facto method for hypergraph representation learning. However, HGNN aims at single hypergraph learning and uses a pre-
Huang, Jing, Huang, Xiaolin, Yang, Jie
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Spectral Detection on Sparse Hypergraphs [PDF]
, 2015 We consider the problem of the assignment of nodes into communities from a set of hyperedges, where every hyperedge is a noisy observation of the community assignment of the adjacent nodes.
Angelini, Maria Chiara +3 more
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Hypergraph convolutional neural network-based clustering technique
IAES International Journal of Artificial Intelligence (IJ-AI), 2022 This paper constitutes the novel hypergraph convolutional neural networkbased clustering technique. This technique is employed to solve the clustering problem for the Citeseer dataset and the Cora dataset. Each dataset contains the feature matrix and the incidence matrix of the hypergraph (i.e., constructed from the feature matrix).
Loc H. Tran, Nguyen Trinh, Linh H. Tran
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