Abstract
The Graph of Words Embedding consists in mapping every graph in a given dataset to a feature vector by counting unary and binary relations between node attributes of the graph. It has been shown to perform well for graphs with discrete label alphabets. In this paper we extend the methodology to graphs with n-dimensional continuous attributes by selecting node representatives. We propose three different discretization procedures for the attribute space and experimentally evaluate the dependence on both the selector and the number of node representatives. In the context of graph classification, the experimental results reveal that on two out of three public databases the proposed extension achieves superior performance over a standard reference system.
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References
Conte, D., Foggia, P., Sansone, C., Vento, M.: Thirty years of graph matching in pattern recognition. International Journal of Pattern Recognition and Artificial Intelligence 18(3), 265–298 (2004)
Luo, B., Wilson, R.C., Hancock, E.R.: Spectral embedding of graphs. Pattern Recognition 36(10), 2213–2230 (2003)
Robles-Kelly, A., Hancock, E.R.: A Riemannian approach to graph embedding. Pattern Recognition 40(3), 1042–1056 (2007)
Emms, D., Wilson, R.C., Hancock, E.R.: Graph Embedding using a Quasi-Quantum Analogue of the Hitting Times of Continuous Time Quantum Walks. Quantum Information and Computation 3-4(9), 231–254 (2009)
Riesen, K., Bunke, H.: Graph Classification and Clustering Based on Vector Space Embedding. World Scientific, Singapore (2010)
Gibert, J., Valveny, E., Bunke, H.: Graph of Words Embedding for Molecular Structure-Activity Relationship Analysis. In: Bloch, I., Cesar Jr., R.M. (eds.) CIARP 2010. LNCS, vol. 6419, pp. 30–37. Springer, Heidelberg (2010)
Dance, C., Willamowski, J., Fan, L., Bray, C., Csurka, G.: Visual categorization with bags of keypoints. In: ECCV International Workshop on Statistical Learning in Computer Vision, pp. 1–22 (2004)
Jain, A., Murty, M., Flynn, P.: Data Clustering: A review. ACM Computing Surveys 31(3), 264–323 (1999)
Cha, S., Srihari, S.N.: On measuring the distance between histograms. Pattern Recognition 35(6), 1355–1370 (2002)
Riesen, K., Bunke, H.: IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning. In: da Vitoria Lobo, N., Kasparis, T., Roli, F., Kwok, J.T., Georgiopoulos, M., Anagnostopoulos, G.C., Loog, M. (eds.) S+SSPR 2008. LNCS, vol. 5342, pp. 287–297. Springer, Heidelberg (2008)
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Gibert, J., Valveny, E., Bunke, H. (2011). Vocabulary Selection for Graph of Words Embedding. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds) Pattern Recognition and Image Analysis. IbPRIA 2011. Lecture Notes in Computer Science, vol 6669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21257-4_27
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DOI: https://doi.org/10.1007/978-3-642-21257-4_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21256-7
Online ISBN: 978-3-642-21257-4
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