Abstract
In general, classifying graphs with labelled nodes (also known as labelled graphs) is a more difficult task than classifying graphs with unlabelled nodes. In this work, we decompose the labelled graphs into unlabelled subgraphs with respect to the labels, and describe these decomposed subgraphs with the travelling matrices. By utilizing the travelling matrices to calculate the dissimilarity for all pairs of subgraphs with the JoEig approach [6], we can build a base classifier in the dissimilarity space for each label. By combining these label base classifiers with the global structure base classifiers built on dissimilarities of graphs considering the full adjacency matrices and the full travelling matrices, respectively, we can solve the labelled graph classification problem with the multiple classifier system.
We acknowledge financial support from the FET programme within the EU FP7, under the SIMBAD project (contract 213250).
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References
Bunke, H., Irniger, C., Neuhaus, M.: Graph Matching - Challenges and Potential Solutions. In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 1–10. Springer, Heidelberg (2005)
Bunke, H., Riesen, K.: Graph Classification Based on Dissimilarity Space Embedding. In: da Vitoria, L., et al. (eds.) Proc. SSSPR 2008, Structural, Syntacic, and Statistical Pattern Recognition, Florida, USA. LNCS, vol. 5342, pp. 996–1007. Springer, Heidelberg (2008)
Dijkstra, E.W.: A Note on Two Problems in Connexion with Graphs. Numerische Mathematik 1, 269–271 (1959)
Duin, R.P.W., Juszczak, P., Paclik, P., Pȩkalska, E., de Ridder, D., Tax, D.M.J.: PRTOOLS4, A Matlab Toolbox for Pattern Recognition, The Netherlands, Delft University of Technology. ICT Group (2004), http://www.prtools.org
Kuncheva, L.I.: Combining Pattern Classifiers. Methods and Algorithms. Wiley, Chichester (2004)
Lee, W.J., Duin, R.P.W.: An Inexact Graph Comparison Approach in Joint Eigenspace. In: Proc. SSSPR 2008, Structural, Syntacic, and Statistical Pattern Recognition, Florida, USA. LNCS, vol. 5342, pp. 35–44. Springer, Heidelberg (2008)
Neuhaus, M., Bunke, H.: Edit Distance-Based Kernel Functions for Structural Pattern Classification. Pattern Recognition 39, 1852–1863 (2006)
Pȩkalska, E., Duin, R.P.W.: The Dissimilarity Representation for Pattern Recognition. In: Fundations and Applications. World Scientific, Singapore (2005)
Qiu, H.J., Hancock, E.R.: Spectral Simplication of Graphs. In: Proceedings of the 8th European Conference on Computer Vision, Czech Republic, pp. 114–126 (2004)
Riesen, K., Bunke, H.: Classifier Ensembles for Vector Space Embedding of Graphs. In: Haindl, M., Kittler, J., Roli, F. (eds.) MCS 2007. LNCS, vol. 4472, pp. 220–230. Springer, Heidelberg (2007)
Riesen, K., Bunke, H.: IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning. In: da Vitoria, L., et al. (eds.) Proc. SSSPR 2008, Structural, Syntacic, and Statistical Pattern Recognition, Florida, USA. LNCS, vol. 5342, pp. 287–297. Springer, Heidelberg (2008)
Schenker, A., Bunke, H., Last, M., Kandel, A.: Building Graph-Based Classifier Ensembles by Random Node Selection. In: Roli, F., Kittler, J., Windeatt, T. (eds.) MCS 2004. LNCS, vol. 3077, pp. 214–222. Springer, Heidelberg (2004)
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Lee, WJ., Duin, R.P.W. (2009). A Labelled Graph Based Multiple Classifier System. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2009. Lecture Notes in Computer Science, vol 5519. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02326-2_21
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DOI: https://doi.org/10.1007/978-3-642-02326-2_21
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