GRAPH RECOGNITION BY SERIATION AND FREQUENT SUBSTRUCTURES MINING

Lorenzo Livi, Guido Del Vescovo, Antonello Rizzi

2012

Abstract

Many interesting applications of Pattern Recognition techniques can take advantage in dealing with labeled graphs as input patterns. To this aim the most important issue is the definition of a dissimilarity measure between graphs. In this paper we propose a representation technique able to characterize the input graphs as real valued feature vectors, allowing the use of standard classification systems. This procedure consists in two distinct stages. In the first step a labeled graph is transformed into a sequence of its vertices, ordered according to a given criterion. In a second step this sequence is mapped into a real valued vector. To perform the latter stage, we propose a novel Granular Computing procedure searching for frequent substructures, called GRADIS. This algorithm is in charge of the inexact substructures identification and of the embedding of the sequenced graphs using the symbolic histogram approach. Tests have been performed by synthetically generating a set of graph classification problem instances with the aim to measure system performances when dealing with different types of graphs, as well when increasing problem hardness.

References

  1. Bargiela, A. and Pedrycz, W. (2003). Granular computing: an introduction. Number v. 2002 in Kluwer international series in engineering and computer science. Kluwer Academic Publishers.
  2. Borgwardt, K. M., Ong, C. S., Sch önauer, S., Vishwanathan, S. V. N., Smola, A. J., and Kriegel, H.-P. (2005). Protein function prediction via graph kernels. Bioinformatics, 21:47-56.
  3. Del Vescovo, G., Livi, L., Rizzi, A., and Frattale Mascioli, F. M. (2011). Clustering structured data with the SPARE library. In Proceeding of 2011 4th IEEE Int. Conf. on Computer Science and Information Technology, volume 9, pages 413-417.
  4. Del Vescovo, G. and Rizzi, A. (2007). Automatic classification of graphs by symbolic histograms. In Proceedings of the 2007 IEEE International Conference on Granular Computing, GRC 7807, pages 410-416, San Jose, CA, USA. IEEE Computer Society.
  5. Kuramochi, M. and Karypis, G. (2002). An efficient algorithm for discovering frequent subgraphs. Technical report, IEEE Transactions on Knowledge and Data Engineering.
  6. Neuhaus, M., Riesen, K., and Bunke, H. (2006). Fast suboptimal algorithms for the computation of graph edit distance. In Structural, Syntactic, and Statistical Pattern Recognition. LNCS, pages 163-172. Springer.
  7. Pe¸kalska, E. and Duin, R. (2005). The dissimilarity representation for pattern recognition: foundations and applications. Series in machine perception and artificial intelligence. World Scientific.
  8. Riesen, K. and Bunke, H. (2009). Approximate graph edit distance computation by means of bipartite graph matching. Image Vision Comput., 27:950-959.
  9. Riesen, K. and Bunke, H. (2010). Graph Classification and Clustering Based on Vector Space Embedding. Series in Machine Perception and Artificial Intelligence. World Scientific Pub Co Inc.
  10. Rizzi, A., Panella, M., and Frattale Mascioli, F. M. (2002). Adaptive resolution min-max classifiers. IEEE Transactions on Neural Networks, 13:402-414.
  11. Robles-Kelly, A. and Hancock, E. R. (2005). Graph edit distance from spectral seriation. IEEE Trans. Pattern Anal. Mach. Intell., 27:365-378.
  12. Vishwanathan, S. V. N., Borgwardt, K. M., Kondor, R. I., and Schraudolph, N. N. (2008). Graph kernels. CoRR, abs/0807.0093.
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Paper Citation


in Harvard Style

Livi L., Del Vescovo G. and Rizzi A. (2012). GRAPH RECOGNITION BY SERIATION AND FREQUENT SUBSTRUCTURES MINING . In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8425-98-0, pages 186-191. DOI: 10.5220/0003733201860191


in Bibtex Style

@conference{icpram12,
author={Lorenzo Livi and Guido Del Vescovo and Antonello Rizzi},
title={GRAPH RECOGNITION BY SERIATION AND FREQUENT SUBSTRUCTURES MINING},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2012},
pages={186-191},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003733201860191},
isbn={978-989-8425-98-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - GRAPH RECOGNITION BY SERIATION AND FREQUENT SUBSTRUCTURES MINING
SN - 978-989-8425-98-0
AU - Livi L.
AU - Del Vescovo G.
AU - Rizzi A.
PY - 2012
SP - 186
EP - 191
DO - 10.5220/0003733201860191