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Graph Neural Network Learning in Large Graphs - A Critical Review


Affiliations
1 Department of Computer Science, University of Mumbai, India
     

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Graph Neural Networks have been extensively used to learn non-Euclidian structures like graphs. There have been several attempts to improve the training efficiency and to reduce the learning complexity in modelling of large graph datasets. In this paper we have reviewed the approaches which perform convolutions to model large graphs for classification and prediction. We have critically analysed each of these approaches and veracity of their claims of reduced complexity and have reported their shortcomings. We have further analysed the approaches from graph-dataset perspective.

Keywords

Graph Neural Networks, Graph Convolutional Networks, Graph Representation Learning, Large Graph Dataset.
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  • Graph Neural Network Learning in Large Graphs - A Critical Review

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Authors

Ashish Gavande
Department of Computer Science, University of Mumbai, India
Sushil Kulkarni
Department of Computer Science, University of Mumbai, India

Abstract


Graph Neural Networks have been extensively used to learn non-Euclidian structures like graphs. There have been several attempts to improve the training efficiency and to reduce the learning complexity in modelling of large graph datasets. In this paper we have reviewed the approaches which perform convolutions to model large graphs for classification and prediction. We have critically analysed each of these approaches and veracity of their claims of reduced complexity and have reported their shortcomings. We have further analysed the approaches from graph-dataset perspective.

Keywords


Graph Neural Networks, Graph Convolutional Networks, Graph Representation Learning, Large Graph Dataset.

References