Abstract
Although graph embedding has recently been used to extend statistical pattern recognition techniques to the graph domain, some existing embeddings are usually computationally expensive as they rely on classical graph-based operations. In this paper we present a new way to embed graphs into vector spaces by first encapsulating the information stored in the original graph under another graph representation by clustering the attributes of the graphs to be processed. This new representation makes the association of graphs to vectors an easy step by just arranging both node attributes and the adjacency matrix in the form of vectors. To test our method, we use two different databases of graphs whose nodes attributes are of different nature. A comparison with a reference method permits to show that this new embedding is better in terms of classification rates, while being much more faster.
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Conte, D., Foggia, P., Sansone, C., Vento, M.: Thirty years of graph matching in pattern recognition. Int. 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)
Emms, D., Wilson, R.C., Hancock, E.R.: Graph Embedding Using Quantum Commute Times. Graph-based Representations in Pattern Recognition, 371–382 (2007)
Bunke, H., Riesen, K.: Recent Developments in Graph Classification and Clustering using Graph Embedding Kernels. Pattern Recognition in Information Systems, 3–13 (2008)
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)
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)
Nene, S., Nayar, S., Murase, H.: Columbia Object Image Library: COIL-100. Technical report, Dept. of Computer Science, Columbia University, New York (1996)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001) Software available at, http://www.csie.ntu.edu.tw/~cjlin/libsvm
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Gibert, J., Valveny, E. (2010). Graph Embedding Based on Nodes Attributes Representatives and a Graph of Words Representation. In: Hancock, E.R., Wilson, R.C., Windeatt, T., Ulusoy, I., Escolano, F. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2010. Lecture Notes in Computer Science, vol 6218. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14980-1_21
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DOI: https://doi.org/10.1007/978-3-642-14980-1_21
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