Results 261 to 270 of about 56,309 (276)
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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 +5 more
semanticscholar +1 more source
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 +5 more
semanticscholar +1 more source
Hypergraph Convolution on Nodes-Hyperedges Network for Semi-Supervised Node Classification
ACM Transactions on Knowledge Discovery from Data, 2022Hypergraphs have shown great power in representing high-order relations among entities, and lots of hypergraph-based deep learning methods have been proposed to learn informative data representations for the node classification problem.
Hanrui Wu, Michael K. Ng
semanticscholar +1 more source
Mathematical Programming, 1997
We consider the capacitated minimum cost flow problem on directed hypergraphs. We define spanning hypertrees so generalizing the spanning tree of a standard graph, and show that, like in the standard and in the generalized minimum cost flow problems, a correspondence exists between bases and spanning hypertrees. Then, we show that, like for the network
CAMBINI, RICCARDO +2 more
openaire +3 more sources
We consider the capacitated minimum cost flow problem on directed hypergraphs. We define spanning hypertrees so generalizing the spanning tree of a standard graph, and show that, like in the standard and in the generalized minimum cost flow problems, a correspondence exists between bases and spanning hypertrees. Then, we show that, like for the network
CAMBINI, RICCARDO +2 more
openaire +3 more sources
Hypergraph Collaborative Network on Vertices and Hyperedges
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022In many practical datasets, such as co-citation and co-authorship, relationships across the samples are more complex than pair-wise. Hypergraphs provide a flexible and natural representation for such complex correlations and thus obtain increasing ...
Hanrui Wu, Yuguang Yan, Michael K. Ng
semanticscholar +1 more source
Music Recommendation via Hypergraph Embedding
IEEE Transactions on Neural Networks and Learning Systems, 2022In recent years, we have witnessed an ever wider spread of multimedia streaming platforms (e.g., Netflix, Spotify, and Amazon). Hence, it has become more and more essential to provide such systems with advanced recommendation facilities, in order to ...
Valerio La Gatta +4 more
semanticscholar +1 more source
Hypergraph Learning: Methods and Practices
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020Hypergraph learning is a technique for conducting learning on a hypergraph structure. In recent years, hypergraph learning has attracted increasing attention due to its flexibility and capability in modeling complex data correlation.
Yue Gao +5 more
semanticscholar +1 more source
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 +4 more
openaire +6 more sources
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 +4 more
openaire +6 more sources
Dual Channel Hypergraph Collaborative Filtering
Knowledge Discovery and Data Mining, 2020Collaborative 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 +5 more
semanticscholar +1 more source
Targeting attack hypergraph networks.
Chaos, 2022In modern systems, from brain neural networks to social group networks, pairwise interactions are not sufficient to express higher-order relationships. The smallest unit of their internal function is not composed of a single functional node but results ...
Hao Peng +5 more
semanticscholar +1 more source
Hypergraph Neural Network for Skeleton-Based Action Recognition
IEEE Transactions on Image Processing, 2021Recently, 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 +4 more
semanticscholar +1 more source