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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