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

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
Main Authors: Atul Kumar Verma, Rahul Saxena, Mahipal Jadeja, Vikrant Bhateja, Jerry Chun-Wei Lin
Format: Article
Language:English
Published: MDPI AG 2023-01-01
Series:Applied Sciences
Subjects:
graph convolution network (GCN)
graph attention network (GAT)
network centrality
semi-supervised node classification
Online Access:https://www.mdpi.com/2076-3417/13/2/847

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https://www.mdpi.com/2076-3417/13/2/847

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