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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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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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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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On the Expressiveness and Generalization of Hypergraph Neural Networks
CoRR, 2023 Learning on Graphs Conference (LoG ...
Zhezheng Luo +3 more
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Molecular hypergraph neural networks
The Journal of Chemical PhysicsGraph neural networks (GNNs) have demonstrated promising performance across various chemistry-related tasks. However, conventional graphs only model the pairwise connectivity in molecules, failing to adequately represent higher order connections, such as multi-center bonds and conjugated structures.
Junwu Chen, Philippe Schwaller
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Preventing Over-Smoothing for Hypergraph Neural Networks
CoRR, 2022 In recent years, hypergraph learning has attracted great attention due to its capacity in representing complex and high-order relationships. However, current neural network approaches designed for hypergraphs are mostly shallow, thus limiting their ability to extract information from high-order neighbors.
Chen, Guanzi +3 more
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Node Classification Method Based on Hierarchical Hypergraph Neural Network
SensorsHypergraph neural networks have gained widespread attention due to their effectiveness in handling graph-structured data with complex relationships and multi-dimensional interactions.
Feng Xu +3 more
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Text Classification Based on Feature Fusion of Dual Hypergraph Neural Networks [PDF]
Jisuanji gongchengIn recent years, Graph Neural Networks (GNNs) have been widely used for text classification tasks. Current models based on GNNs first model the text as a graph and then use GNNs to propagate and aggregate the features of the text graph.
ZHENG Cheng, LI Pengfei
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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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