Additive Angular Margin Loss in Deep Graph Neural Network Classifier for Learning Graph Edit Distance

Additive Angular Margin Loss in Deep Graph Neural Network Classifier for Learning Graph Edit Distance

The recent success of graph neural networks (GNNs) in the area of pattern recognition (PR) has increased the interest of researchers to use these frameworks in non-euclidean structures. This non-euclidean structure includes graphs or manifolds that are called geometric deep learning (GDL). It has op...

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Bibliographic Details
Main Authors: Nadeem Iqbal Kajla, Malik Muhammad Saad Missen, Muhammad Muzzamil Luqman, Mickael Coustaty, Arif Mehmood, Gyu Sang Choi
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Graph neural networks (GNN)
graph learning
graph edit distance (GED)
geometric deep learning (GDL)
loss margin
loss function
Online Access:https://ieeexplore.ieee.org/document/9250458/

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https://ieeexplore.ieee.org/document/9250458/

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