Results 21 to 30 of about 1,216 (173)
From Hypergraph Energy Functions to Hypergraph Neural Networks
CoRR, 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 proposed, in large part building upon precursors from the more traditional graph neural network (GNN ...
Yuxin Wang 0005 +4 more
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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 ...
Yifan Feng 0001 +4 more
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Android Malware Detection Based on Hypergraph Neural Networks
Applied Sciences, 2023 Android has been the most widely used operating system for mobile phones over the past few years. Malicious attacks against android are a major privacy and security concern. Malware detection techniques for android applications are therefore significant.
Dehua Zhang +6 more
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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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On the Expressiveness and Generalization of Hypergraph Neural Networks
CoRR, 2023 Learning on Graphs Conference (LoG ...
Zhezheng Luo +3 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-
Jing Huang, Xiaolin Huang, Jie Yang 0002
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Message Passing Neural Networks for Hypergraphs
, 2022 Hypergraph representations are both more efficient and better suited to describe data characterized by relations between two or more objects. In this work, we present a new graph neural network based on message passing capable of processing hypergraph-structured data.
Sajjad Heydari, Lorenzo Livi
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, 2023 AbstractWith the development of deep learning on high-order correlations, hypergraph neural networks have received much attention in recent years. Generally, the neural networks on hypergraph can be divided into two categories, including the spectral-based methods and the spatial-based methods.
Qionghai Dai, Yue Gao
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Multi-Order Hypergraph Convolutional Neural Network for Dynamic Social Recommendation System
IEEE Access, 2022 Recently, online social networks have enriched the users’ lives greatly and social recommendation systems make it easier for users to discover more information that they are interested in.
Yu Wang, Qilong Zhao
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Hierarchical Hypergraph-based Attention Neural Network for Service Recommendation [PDF]
Jisuanji kexueWith the rapid growth of various services and APIs on the Internet and the Web,it has become increasingly challenging for developers to quickly and accurately find APIs that meet their needs,thus requiring an efficient recommendation system.Currently,the
YANG Dongsheng, WANG Guiling, ZHENG Xin
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