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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 3/2015

01-06-2015 | Original Article

Hybrid neural network for classification of graph structured data

Authors: R. B. Gnana Jothi, S. M. Meena Rani

Published in: International Journal of Machine Learning and Cybernetics | Issue 3/2015

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Abstract

Multilayer perceptron (MLP) with recurrent architecture is proposed for stabilizing the state vector, which represents the characteristics of the nodes in a graph, to classify the graph structured data. M number of input and output networks are constructed for the M node undirected graphs for classifying graph structured data. Output of every input network represents the characteristics of the node as a state vector. The output of each input MLP is also taken as input for the same network along with output of neighboring node’s MLP. Both the input and output networks are trained by backpropagation. The proposed approach is implemented on the standard benchmark classification problems namely mutagenesis problem, subgraph matching problem and clique problem. Simulation results show that best accuracy in classification is obtained with minimum computational complexity.
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Metadata
Title
Hybrid neural network for classification of graph structured data
Authors
R. B. Gnana Jothi
S. M. Meena Rani
Publication date
01-06-2015
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 3/2015
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-014-0230-8

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