Results 161 to 170 of about 47,538 (182)

Multi-way relation-enhanced hypergraph representation learning for anti-cancer drug synergy prediction

Bioinform., 2022
MOTIVATION Drug combinations have exhibited promise in treating cancers with less toxicity and fewer adverse reactions. However, in vitro screening of synergistic drug combinations is time-consuming and labour-intensive because of the combinatorial ...
Xuan Liu, Congzhi Song, Shichao Liu, Menglu Li, Xionghui Zhou, Wen Zhang   +5 more
semanticscholar   +1 more source

Sequence Hypergraphs

2016
We introduce sequence hypergraphs by extending the concept of a directed edge (from simple directed graphs) to hypergraphs. Specifically, every hyperedge of a sequence hypergraph is defined as a sequence of vertices (imagine it as a directed path). Note that this differs substantially from the standard definition of directed hypergraphs.
Böhmovà, Kateřina, Chalopin, Jèrèmie, Mihalàk, Matùš, PROIETTI, GUIDO, Widmayer, Peter   +4 more
openaire   +5 more sources

Dual Channel Hypergraph Collaborative Filtering

Knowledge Discovery and Data Mining, 2020
Collaborative filtering (CF) is one of the most popular and important recommendation methodologies in the heart of numerous recommender systems today. Although widely adopted, existing CF-based methods, ranging from matrix factorization to the emerging ...
Shuyi Ji, Yifan Feng, Rongrong Ji, Xibin Zhao, Wanwan Tang, Yue Gao   +5 more
semanticscholar   +1 more source

Clustering ensemble via structured hypergraph learning

Information Fusion, 2022
Clustering ensemble integrates multiple base clustering results to obtain a consensus result and thus improves the stability and robustness of the single clustering method. Since it is natural to use a hypergraph to represent the multiple base clustering
Peng Zhou, Xia Wang, Liang Du, Xuejun Li
semanticscholar   +1 more source

Hypergraph-enhanced Dual Semi-supervised Graph Classification

International Conference on Machine Learning
In this paper, we study semi-supervised graph classification, which aims at accurately predicting the categories of graphs in scenarios with limited labeled graphs and abundant unlabeled graphs.
Wei Ju, Zhengyan Mao, Siyu Yi, Yifang Qin, Yiyang Gu, Zhiping Xiao, Yifan Wang, Xiao Luo, Ming Zhang   +8 more
semanticscholar   +1 more source

Hypergraph Neural Network for Skeleton-Based Action Recognition

IEEE Transactions on Image Processing, 2021
Recently, skeleton-based human action recognition has attracted a lot of research attention in the field of computer vision. Graph convolutional networks (GCNs), which model the human body skeletons as spatial-temporal graphs, have shown excellent ...
Xiaoke Hao, Jie Li, Yingchun Guo, Tao Jiang, Ming Yu   +4 more
semanticscholar   +1 more source

IIS-Hypergraphs

SIAM Journal on Discrete Mathematics, 1996
A subsystem of an inconsistent set of inequalities is an irreducibly inconsistent subsystem (IIS) if it is inconsistent and if it has no inconsistent proper subsystem. Each IIS can be considered the edge of a hypergraph. The paper presents several properties of this special class of hypergraphs (IIS-hypergraphs).
openaire   +1 more source

Dynamic Hypergraph Structure Learning for Multivariate Time Series Forecasting

IEEE Transactions on Big Data
Multivariate time series forecasting plays an important role in many domain applications, such as air pollution forecasting and traffic forecasting. Modeling the complex dependencies among time series is a key challenging task in multivariate time series
Shun-Kai Wang, Yong Zhang, Xuanqi Lin, Yongli Hu, Qingming Huang, Baocai Yin   +5 more
semanticscholar   +1 more source

Hypergraph isomorphism using association hypergraphs

Pattern Recognition Letters, 2019
Abstract Association graphs represent a classical tool to deal with the graph matching problem and recently the idea has been generalized to the case of hypergraphs. In this article, the potential of this approach is explored. The proposed framework uses a class of dynamical systems derived from the Baum-Eagon inequality in order to find the maximum (
Giulia Sandi, Sebastiano Vascon, Marcello Pelillo   +2 more
openaire   +1 more source

T-HyperGNNs: Hypergraph Neural Networks via Tensor Representations

IEEE Transactions on Neural Networks and Learning Systems
Hypergraph neural networks (HyperGNNs) are a family of deep neural networks designed to perform inference on hypergraphs. HyperGNNs follow either a spectral or a spatial approach, in which a convolution or message-passing operation is conducted based on ...
Fuli Wang, K. Pena-Pena, Wei Qian, Gonzalo R. Arce   +3 more
semanticscholar   +1 more source