Determining the optimal number of GAT and GCN layers for node classification in graph neural networks

Determining the optimal number of GAT and GCN layers for node classification in graph neural networks

Node classification in complex networks plays an important role including social network analysis and recommendation systems. Some graph neural networks such as Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) have emerged as effective approaches for achieving high-performance c...

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Bibliographic Details
Main Authors: Noor, Humaira, Islam, Niful, Hossain Mukta, Md Saddam, Nur Shazwani, Kamarudin, Khan Raiaan, Mohaimenul Azam, Azam, Sami
Format: Conference or Workshop Item
Language:English
English
Published: Institute of Electrical and Electronics Engineers Inc. 2023
Subjects:
QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
Online Access:http://umpir.ump.edu.my/id/eprint/40357/1/Determining%20the%20optimal%20number%20of%20GAT%20and%20GCN.pdf
http://umpir.ump.edu.my/id/eprint/40357/2/Determining%20the%20optimal%20number%20of%20GAT%20and%20GCN%20layers%20for%20node%20classification%20in%20graph%20neural%20networks_ABS.pdf

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http://umpir.ump.edu.my/id/eprint/40357/1/Determining%20the%20optimal%20number%20of%20GAT%20and%20GCN.pdf
http://umpir.ump.edu.my/id/eprint/40357/2/Determining%20the%20optimal%20number%20of%20GAT%20and%20GCN%20layers%20for%20node%20classification%20in%20graph%20neural%20networks_ABS.pdf

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