Bet-GAT: An Efficient Centrality-Based Graph Attention Model for Semi-Supervised Node Classification

Graph Neural Networks (GNNs) have witnessed great advancement in the field of neural networks for processing graph datasets. Graph Convolutional Networks (GCNs) have outperformed current models/algorithms in accomplishing tasks such as semi-supervised node classification, link prediction, and graph...

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Bibliographic Details
Published in:Applied sciences 2023-01, Vol.13 (2), p.847
Main Authors: Verma, Atul Kumar, Saxena, Rahul, Jadeja, Mahipal, Bhateja, Vikrant, Lin, Jerry Chun-Wei
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Classification
Computers
Datasets
Deep learning
graph attention network (GAT)
graph convolution network (GCN)
Graph neural networks
Information processing
Machine learning
Mathematical models
Methods
Neighborhoods
network centrality
Neural networks
Nodes
Propagation
Route optimization
semi-supervised node classification
Social networks
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Summary:Graph Neural Networks (GNNs) have witnessed great advancement in the field of neural networks for processing graph datasets. Graph Convolutional Networks (GCNs) have outperformed current models/algorithms in accomplishing tasks such as semi-supervised node classification, link prediction, and graph classification. GCNs perform well even with a very small training dataset. The GCN framework has evolved to Graph Attention Model (GAT), GraphSAGE, and other hybrid frameworks. In this paper, we effectively usd the network centrality approach to select nodes from the training set (instead of a traditional random selection), which is fed into GCN (and GAT) to perform semi-supervised node classification tasks. This allows us to take advantage of the best positional nodes in the network. Based on empirical analysis, we choose the betweenness centrality measure for selecting the training nodes. We also mathematically justify why our proposed technique offers better training. This novel training technique is used to analyze the performance of GCN and GAT models on five benchmark networks—Cora, Citeseer, PubMed, Wiki-CS, and Amazon Computers. In GAT implementations, we obtain improved classification accuracy compared to the other state-of-the-art GCN-based methods. Moreover, to the best of our knowledge, the results obtained for Citeseer, Wiki- CS, and Amazon Computer datasets are the best compared to all the existing node classification methods.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13020847