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Graph Classification Using Back Propagation Learning Algorithms

Graph Classification Using Back Propagation Learning Algorithms

Abhijit Bera, Mrinal Kanti Ghose, Dibyendu Kumar Pal
Copyright: © 2020 |Volume: 11 |Issue: 2 |Pages: 12
ISSN: 2640-4265|EISSN: 2640-4273|EISBN13: 9781799809180|DOI: 10.4018/ijsssp.2020070101
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MLA

Bera, Abhijit, et al. "Graph Classification Using Back Propagation Learning Algorithms." IJSSSP vol.11, no.2 2020: pp.1-12. http://doi.org/10.4018/ijsssp.2020070101

APA

Bera, A., Ghose, M. K., & Pal, D. K. (2020). Graph Classification Using Back Propagation Learning Algorithms. International Journal of Systems and Software Security and Protection (IJSSSP), 11(2), 1-12. http://doi.org/10.4018/ijsssp.2020070101

Chicago

Bera, Abhijit, Mrinal Kanti Ghose, and Dibyendu Kumar Pal. "Graph Classification Using Back Propagation Learning Algorithms," International Journal of Systems and Software Security and Protection (IJSSSP) 11, no.2: 1-12. http://doi.org/10.4018/ijsssp.2020070101

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Abstract

Due to the propagation of graph data, there has been a sharp focus on developing effective methods for classifying the graph object. As most of the proposed graph classification techniques though effective are constrained by high computational overhead, there is a consistent effort to improve upon the existing classification algorithms in terms of higher accuracy and less computational time. In this paper, an attempt has been made to classify graphs by extracting various features and selecting the important features using feature selection algorithms. Since all the extracted graph-based features need not be equally important, only the most important features are selected by using back propagation learning algorithm. The results of the proposed study of feature-based approach using back propagation learning algorithm lead to higher classification accuracy with faster computational time in comparison to other graph kernels. It also appears to be more effective for large unlabeled graphs.

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