Results 31 to 40 of about 5,865 (183)
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
openaire +1 more source
IEEE Access, 2023 With the increasingly competitive job market, the employment issue for college graduates has received more and more attention. Predicting graduation development can help students understand their suitable graduation development, thus easing the pressure ...
Yong Ouyang +4 more
doaj +1 more source
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-
Huang, Jing, Huang, Xiaolin, Yang, Jie
openaire +2 more sources
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
doaj +2 more sources
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
doaj +1 more source
Hypergraph convolutional neural network-based clustering technique
IAES International Journal of Artificial Intelligence (IJ-AI), 2022 This paper constitutes the novel hypergraph convolutional neural networkbased clustering technique. This technique is employed to solve the clustering problem for the Citeseer dataset and the Cora dataset. Each dataset contains the feature matrix and the incidence matrix of the hypergraph (i.e., constructed from the feature matrix).
Loc H. Tran, Nguyen Trinh, Linh H. Tran
openaire +2 more sources
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
openaire +2 more sources
, 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
openaire +1 more source
From Hypergraph Energy Functions to Hypergraph Neural Networks
, 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 ...
Wang, Yuxin +4 more
openaire +2 more sources
T-HyperGNNs: Hypergraph Neural Networks Via Tensor Representations
IEEE Transactions on Neural Networks and Learning Systems, 2023 <p>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 a hypergraph algebraic descriptor.
Fuli Wang +3 more
openaire +2 more sources