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Hybrid neural network for classification of graph structured data

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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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References

  1. Atiya A (1988) Learning on a general frame work, neural information processing systems. In: Anderson DZ (ed) AIP, New York, pp 22–30

  2. Bianchini M, Maggini M, Sarti L, Scarselli F (2005) Recursive neural networks for processing graphs with labeled edges: theory and applications. Neural Netw 18:1040–1050

    Article  Google Scholar 

  3. Bianchini M, Gori M, Sarti M, Scarselli F (2006) Recursive processing of cyclic graphs. IEEE Trans Neural Netw 17(1):10–17.

    Article  Google Scholar 

  4. Bong DBL, Tan JYB, Lai KC (2008) Application of multilayer perceptron with backpropagation algorithm and regression analysis for long-term forecast of electricity demand—a comparison. Int Conf Electron Des 3:1–5

    Google Scholar 

  5. Chatrand G (2006) Introduction to Graph Theory. Tata Mc Graw-Hill, New Delhi

  6. De Raedt L, Blockeel H (1997) Using logical desicion trees for clustering. Lecture notes in artificial intelligence, vol 1297. pp 133–141

  7. Debnath AK, Lopex de Compandre R, Debnath G, Schusterman A, Hansch C (1991) Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds correlation with molecular orbital energies and hydrophobicity. J Med Chem 34:786–797

    Article  Google Scholar 

  8. Elman J (1990) Finding structure in time. Cogn Sci 14:179–211

    Article  Google Scholar 

  9. Frasconi P, Gori M, Sperduti A (1998) A general framework for adaptive processing of data structures. IEEE Trans Neural Netw 9:768–786

    Article  Google Scholar 

  10. Frasconi P, Gori M, Sperduti A (1997) On the efficient classification of data structures by neural networks. In: Proceedings of Fifteenth International Joint Conference Artificial Neural Networks, vol 2. pp 1066–1071

  11. Jain SK, Patnaik A, Sinha SN (2013) Design of custom made stacked patch antennas: a machine learning algorithm. Int J Mach Learn Cybern 4:189–194

    Article  Google Scholar 

  12. Khotanzad A, Chung C (1998) Application of multilayer perceptron neural networks to vision problems. Neural Comput Appl 7:249–159

    Article  Google Scholar 

  13. Kirsten M (2002) Multirelational distance-based clustering. Ph.D dissertation, School of computer Science, Otto-von-Guericke University, Magdeburg, Germany

  14. Noi LD, Hagenbuchner M, Scarselli F, Tsoi AC (2010) Web spam detection by probability mapping graph self organising maps and graph neural networks, Artificial Neural Networks, ICANN 2010. Lecture notes in computer science, vol 6353. pp 372–381

  15. Pollack JB (1990) Recursive distributed representations. Artifi Intell 46:77–106

    Article  Google Scholar 

  16. Pucci A, Gori M, Hagenbuchner M, Scarselli F, Tsoi AC (2006) Applications of graph neural networks to large-scale recommender systems some results. In: Proceedings of International Multiconference on Computer Science and Information Technology, pp 189–195

  17. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning reprsentations by back-propagation errors. Nature 323:533–536

    Article  Google Scholar 

  18. Savitha R, Suresh S, Sundarajan N (2013) Fast learning complex valued classifiers for real world classification problems. Int J Mach Learn Cybern 4:469–476

    Article  Google Scholar 

  19. Sperduti A (1998) Neural networks for processing data structures, Adaptive processing of sequences and data structures. Lecture Notes in Computer Science, vol 1387. pp 121–144

  20. Sperduti A, Starita A, Goller C (1995) Learning distributed representations for the classification of terms. In: Proceedings of International Joint Conference on Artificial Intelligence, pp 509–515

  21. Sperduti A, Starita T (1997) Supervised neural networks for classification of structures. IEEE Trans Neural Netw 8:714–735

    Article  Google Scholar 

  22. Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2009) The graph neural network model. IEEE Trans Neural Netw 20:61–80

    Article  Google Scholar 

  23. Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2009) Computational capabilities of graph neural networks. IEEE Trans Neural Netw 20:81–102

    Article  Google Scholar 

  24. Scarselli F, Yong SL, Gori M, Hagenbuchner M, Tsoi AC, Maggini M.(2005) Graph neural networks for ranking web pages. In: Proceedings of the 2005 IEEE/WIC/ACM Conference on Web Intelligence, Washington DC, pp 666–672

  25. Srinivasan A, Muggleton S, King R, Sterbberg M (1994) Mutagenesis: Ilp experiments in a non-determinate biological domain. In: Proceedings of 4th International Workshop Inductive logic Programme, pp 217–232

  26. Tsang E, Wang X, Yeung D (2000) Improving learning accuracy of fuzzy decision trees by hybrid neural networks. IEEE Trans Fuzzy Syst 8:601–614

    Article  Google Scholar 

  27. Wang X, Dong C, Fan T (2007) Training T-S norm neural networks to refine weights for fuzzy if-then rules. Neurocomputing 70:2581–2587

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mathematics, V. V. Vanniaperumal College for Women, Virudhunagar, 626001, Tamilnadu, India

    R. B. Gnana Jothi & S. M. Meena Rani

Authors
  1. R. B. Gnana Jothi
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  2. S. M. Meena Rani
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Correspondence to S. M. Meena Rani.

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Gnana Jothi, R.B., Meena Rani, S.M. Hybrid neural network for classification of graph structured data. Int. J. Mach. Learn. & Cyber. 6, 465–474 (2015). https://doi.org/10.1007/s13042-014-0230-8

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  • DOI: https://doi.org/10.1007/s13042-014-0230-8

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