Results 11 to 20 of about 1,157 (178)
Signal Contrastive Enhanced Graph Collaborative Filtering for Recommendation
Data Science and Engineering, 2023 Graph collaborative filtering methods have shown great performance improvements compared with deep neural network-based models. However, these methods suffer from data sparsity and data noise problems.
Zhi-Yuan Li +3 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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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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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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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-
Jinwoo Kim +3 more
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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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On the Expressiveness and Generalization of Hypergraph Neural Networks
CoRR, 2023 Learning on Graphs Conference (LoG ...
Zhezheng Luo +3 more
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