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Graph Random Neural Network for Semi-Supervised Learning on Graphs
[article]
2021
arXiv
pre-print
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We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored. ...
In this paper, we propose a simple yet effective framework -- GRAPH RANDOM NEURAL NETWORKS (GRAND) -- to address these issues. ...
GRAPH RANDOM NEURAL NETWORKS We present the GRAPH RANDOM NEURAL NETWORKS (GRAND) for semi-supervised learning on graphs, as illustrated in Figure 1 . ...
arXiv:2005.11079v4
fatcat:jvxmfcpvsfcadniu3kfqt6jgia
Directed hypergraph neural network
[article]
2022
arXiv
pre-print
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2020 pre-print arXiv:2008.03626v1 fatcat:odmee2iwo5ekniw6bgpd6z3eu4
Among the classic directed graph based semi-supervised learning method, the novel directed hypergraph based semi-supervised learning method, the novel directed hypergraph neural network method that are ...
However, data scientists just have concentrated primarily on developing deep neural network method for un-directed graph. ...
based semi-supervised
learning
Directed graph neural
network
F-directed hypergraph
neural network
B-directed hypergraph
neural network ...
arXiv:2008.03626v2
fatcat:uyk2zndaqvarbbrbldny2t7vee
Semi-Supervised Classification with Graph Convolutional Networks
[article]
2017
arXiv
pre-print
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We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. ...
In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin. ...
ACKNOWLEDGMENTS We would like to thank Christos Louizos, Taco Cohen, Joan Bruna, Zhilin Yang, Dave Herman, Pramod Sinha and Abdul-Saboor Sheikh for helpful discussions. ...
arXiv:1609.02907v4
fatcat:pt7vcc7b5vecflnsu4xdigvceq
Application of three graph Laplacian based semisupervised learning methods to protein function prediction problem
2013
International Journal on Bioinformatics & Biosciences
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In this paper, the un-normalized, symmetric normalized, and random walk graph Laplacian based semi-supervised learning methods will be applied to the integrated network combined from multiple networks ...
Moreover, the accuracy performance measures of these three semi-supervised learning methods for integrated network are much better than the best accuracy performance measures of these three methods for ...
the published) and normalized graph Laplacian based semi-supervised learning methods slightly perform better than the random walk graph Laplacian based semi-supervised learning method using network ( ) ...
doi:10.5121/ijbb.2013.3202
fatcat:pta7isryl5asnh6lboeoq44y7a
A Flexible Generative Framework for Graph-based Semi-supervised Learning
[article]
2019
arXiv
pre-print
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Relational information among the data samples, often encoded in the graph/network structure, is shown to be helpful for these semi-supervised learning tasks. ...
We conduct thorough experiments on benchmark datasets for graph-based semi-supervised learning. Results show that the proposed methods outperform the state-of-the-art models in most settings. ...
Graph Neural Networks for Semi-supervised Learning Another class of methods that have gained great attention recently are the graph neural networks [10, 5, 21] . ...
arXiv:1905.10769v2
fatcat:mtooywzivfevricftsdcjr56ke
Hypergraph based semi-supervised learning algorithms applied to speech recognition problem: a novel approach
[article]
2018
arXiv
pre-print
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Hidden Markov Model method (the current state of the art method applied to speech recognition problem) and graph based semi-supervised learning methods (i.e. the current state of the art network-based ...
Thus, in this paper, the three un-normalized, random walk, and symmetric normalized hypergraph Laplacian based semi-supervised learning methods applied to hypergraph constructed from the feature data of ...
This work is funded by the Ministry of Science and Technology, State-level key program, Research for application and development of information technology and communications, code KC.01.23/11-15. ...
arXiv:1810.12743v1
fatcat:whavzea7fjgtfknja4ctzhuism
Noise-robust classification with hypergraph neural network
2021
Indonesian Journal of Electrical Engineering and Computer Science
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Then, the classic graph based semisupervised learning method, the classic hypergraph based semi-supervised learning method, the graph neural network, the hypergraph neural network, and our proposed hypergraph ...
Moreover, the hypergraph neural network methods are at least as good as the graph neural network.</p> ...
. c) Compare the accuracy performance measures of the classic graph based semi-supervised learning problem, the classic hypergraph based semi-supervised learning problem, the graph neural network method ...
doi:10.11591/ijeecs.v21.i3.pp1465-1473
fatcat:iz7g63by3vgofnrj6gocz5b4zy
Hypergraph and protein function prediction with gene expression data
[article]
2012
arXiv
pre-print
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Thus, in this paper, the three un-normalized, random walk, and symmetric normalized hypergraph Laplacian based semi-supervised learning methods applied to hypergraph constructed from the gene expression ...
However, their average accuracy performance measures of these three methods are much greater than the average accuracy performance measures of un-normalized graph Laplacian based semi-supervised learning ...
full topology of the network, the Artificial Neural Networks, Support Vector Machine, unnormalized, symmetric normalized and random walk graph Laplacian based semi-supervised learning method utilizes ...
arXiv:1212.0388v1
fatcat:sf2aopcbpbcptdfughqvbyqnei
Convolutional Neural Networks On Graphs With Fast Localized Spectral Filtering (Smld'16)
2016
Zenodo
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Presentation given at the Swiss Machine Learning Day (SMLD) (https://www.idiap.ch/workshop/smld2016). ...
ConvNets & Graphs
ConvNets on Graphs
Numerical Experiments
MNIST
20NEWS
Application: Semi-supervised learning
Application: Recurrent Neural Nets
Table: Accuracies of the proposed graph CNN ...
64.64
FC2500-FC500
65.76
GC32
68.26
ConvNets & Graphs
ConvNets on Graphs
Numerical Experiments
MNIST
20NEWS
Application: Semi-supervised learning
Application: Recurrent Neural Nets
Graph ...
doi:10.5281/zenodo.1318411
fatcat:vgrdipopw5h5zcmngwjxzhhv2q
A Generative Bayesian Graph Attention Network for Semi-Supervised Classification on Scarce Data
2021
2021 International Joint Conference on Neural Networks (IJCNN)
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This research focuses on semi-supervised classification tasks, specifically for graph-structured data under datascarce situations. ...
The proposed method is comprehensively evaluated on three graph-based deep learning benchmark data sets. ...
Extensive graph neural network (GNN) based models, including graph convolution networks (GCNs) [3] and graph attention networks (GATs) [4] have been developed for unsupervised, semi-supervised and ...
doi:10.1109/ijcnn52387.2021.9533981
fatcat:r7bssnui4vgkpnt2jqzvcuspiy
Few-Shot Learning with Graph Neural Networks
[article]
2018
arXiv
pre-print
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Besides providing improved numerical performance, our framework is easily extended to variants of few-shot learning, such as semi-supervised or active learning, demonstrating the ability of graph-based ...
By assimilating generic message-passing inference algorithms with their neural-network counterparts, we define a graph neural network architecture that generalizes several of the recently proposed few-shot ...
ACKNOWLEDGMENTS This work was partly supported by Samsung Electronics (Improving Deep Learning using Latent Structure). ...
arXiv:1711.04043v3
fatcat:3fd7knhjorhrncf4h3dwuwwoby
SLGAT: Soft Labels Guided Graph Attention Networks
[chapter]
2020
Lecture Notes in Computer Science
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Graph convolutional neural networks have been widely studied for semi-supervised classification on graph-structured data in recent years. ...
Experimental results on semi-supervised node classification show that SLGAT achieves state-of-the-art performance. ...
Related Work Graph-Based Semi-supervised Learning. ...
doi:10.1007/978-3-030-47426-3_40
fatcat:627gi5flknavfjs5tucadoxf3y
Non-iterative approaches in training feed-forward neural networks and their applications
2018
Soft Computing - A Fusion of Foundations, Methodologies and Applications
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Keywords Non-iterative approaches · Feed-forward neural networks · Neural networks with random weights · Deep learning ...
Focusing on non-iterative approaches in training feed-forward neural networks, this special issue includes 12 papers to share the latest progress, current challenges, and potential applications of this ...
Antonio Di Nola, the Editor-in-Chief of Soft Computing, for his support to edit this special issue. ...
doi:10.1007/s00500-018-3203-0
fatcat:snmmbo3ys5a2plmpsyxowzye6a
Semi-Supervised Node Classification by Graph Convolutional Networks and Extracted Side Information
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2009.13734v1 fatcat:c7c5hqti45hqpcbevq4ewnpb3a
From this perspective, this paper revisits the node classification task in a semi-supervised scenario by graph convolutional networks (GCNs). ...
For both cases, the experiments on synthetic and real-world datasets demonstrate that the proposed model achieves a higher prediction accuracy in comparison to the existing state-of-the-art methods for ...
] , graph attention networks [2] , a variant of attention-based graph neural network for semi-supervised learning [17] , and dual graph convolutional networks [18] . ...
arXiv:2009.13734v2
fatcat:tqk3kcb2abhc3igulyavifxuvi
Semisupervised Learning on Heterogeneous Graphs and its Applications to Facebook News Feed
[article]
2018
arXiv
pre-print
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Graph-based semi-supervised learning is a fundamental machine learning problem, and has been well studied. Most studies focus on homogeneous networks (e.g. citation network, friend network). ...
In the present paper, we propose the Heterogeneous Embedding Label Propagation (HELP) algorithm, a graph-based semi-supervised deep learning algorithm, for graphs that are characterized by heterogeneous ...
Graph-based Semi-supervised Learning Graph-based semi-supervised learning is widely used in network analysis, for prediction/clustering tasks over nodes and edges. ...
arXiv:1805.07479v2
fatcat:a6fcag6nszft7d2hpmash2b2ni
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