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An Edge-Based Matching Kernel for Graphs Through the Directed Line Graphs

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Computer Analysis of Images and Patterns (CAIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9257))

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Abstract

In this paper, we propose a new edge-based matching kernel for graphs. We commence by transforming a graph into a directed line graph. The reasons of using the line graph structure are twofold. First, for a graph, its directed line graph is a dual representation and each vertex of the line graph represents a corresponding edge in the original graph. As a result, we can develop an edge-based matching kernel for graphs by aligning the vertices in their directed line graphs. Second, the directed line graph may expose richer graph characteristics than the original graph. For a pair of graphs, we compute the h-layer depth-based representations rooted at the vertices of their directed line graphs, i.e., we compute the depth-based representations for edges of the original graphs through their directed line graphs. Based on the new representations, we define an edge-based matching method for the pair of graphs by aligning the h-layer depth-based representations computed through the directed line graphs. The new edge-based matching kernel is thus computed by counting the number of matched vertices identified by the matching method on the directed line graphs. Experiments on standard graph datasets demonstrate the effectiveness of our new edge-based matching kernel.

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References

  1. Bai, L., Rossi, L., Bunke, H., Hancock, E.R.: Attributed graph kernels using the Jensen-Tsallis q-differences. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds.) ECML PKDD 2014, Part I. LNCS, vol. 8724, pp. 99–114. Springer, Heidelberg (2014)

    Google Scholar 

  2. Haussler, D.: Convolution kernels on discrete structures. Technical Report UCS-CRL-99-10, UC Santa Cruz (1999)

    Google Scholar 

  3. Kashima, H., Tsuda, K., Inokuchi, A.: Marginalized kernels between labeled graphs. In: ICML, pp. 321–328 (2003)

    Google Scholar 

  4. Borgwardt, K.M., Kriegel, H.: Shortest-path kernels on graphs. In: Proceedings of ICDM, pp. 74–81 (2005)

    Google Scholar 

  5. Aziz, F., Wilson, R.C., Hancock, E.R.: Backtrackless walks on a graph. IEEE Trans. Neural Netw. Learning Syst. 24, 977–989 (2013)

    Article  Google Scholar 

  6. Ren, P., Wilson, R.C., Hancock, E.R.: Graph characterization via ihara coefficients. IEEE Transactions on Neural Networks 22, 233–245 (2011)

    Article  Google Scholar 

  7. Shervashidze, N., Schweitzer, P., van Leeuwen, E.J., Mehlhorn, K., Borgwardt, K.M.: Weisfeiler-lehman graph kernels. Journal of Machine Learning Research 12, 2539–2561 (2011)

    MATH  Google Scholar 

  8. Harchaoui, Z., Bach, F.: Image classification with segmentation graph kernels. In: Proceedings of CVPR (2007)

    Google Scholar 

  9. Bach, F.R.: Graph kernels between point clouds. In: Proceedings of ICML, pp. 25–32 (2008)

    Google Scholar 

  10. Bai, L., Ren, P., Hancock, E.R.: A hypergraph kernel from isomorphism tests. In: 22nd International Conference on Pattern Recognition, ICPR 2014, Stockholm, Sweden, August 24–28, 2014, pp. 3880–3885 (2014)

    Google Scholar 

  11. Bai, L.: Information Theoretic Graph Kernels. University of York, UK (2014)

    Google Scholar 

  12. Bai, L., Ren, P., Bai, X., Hancock, E.R.: A graph kernel from the depth-based representation. In: Fränti, P., Brown, G., Loog, M., Escolano, F., Pelillo, M. (eds.) S+SSPR 2014. LNCS, vol. 8621, pp. 1–11. Springer, Heidelberg (2014)

    Google Scholar 

  13. Bai, L., Hancock, E.R.: Depth-based complexity traces of graphs. Pattern Recognition 47, 1172–1186 (2014)

    Article  Google Scholar 

  14. Bai, L., Hancock, E.R.: Graph kernels from the jensen-shannon divergence. Journal of Mathematical Imaging and Vision 47, 60–69 (2013)

    Article  MATH  Google Scholar 

  15. Escolano, F., Hancock, E., Lozano, M.: Heat diffusion: Thermodynamic depth complexity of networks. Physical Review E 85, 206236 (2012)

    Article  Google Scholar 

  16. Munkres, J.: Algorithms for the assignment and transportation problems. Journal of the Society for Industrial and Applied Mathematics 5 (1957)

    Google Scholar 

  17. Biasotti, S., Marini, S., Mortara, M., Patané, G., Spagnuolo, M., Falcidieno, B.: 3D shape matching through topological structures. In: Nyström, I., Sanniti di Baja, G., Svensson, S. (eds.) DGCI 2003. LNCS, vol. 2886, pp. 194–203. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  18. Shervashidze, N., Vishwanathan, S., Petri, T., Mehlhorn, K., Borgwardt, K.: Efficient graphlet kernels for large graph comparison. Journal of Machine Learning Research 5, 488–495 (2009)

    Google Scholar 

  19. Bai, L., Rossi, L., Torsello, A., Hancock, E.R.: A quantum jensen-shannon graph kernel for unattributed graphs. Pattern Recognition 48, 344–355 (2015)

    Article  Google Scholar 

  20. Chang, C.C., Lin, C.J.: Libsvm: A library for support vector machines (2011). Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm

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Bai, L., Zhang, Z., Wang, C., Hancock, E.R. (2015). An Edge-Based Matching Kernel for Graphs Through the Directed Line Graphs. In: Azzopardi, G., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science(), vol 9257. Springer, Cham. https://doi.org/10.1007/978-3-319-23117-4_8

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  • DOI: https://doi.org/10.1007/978-3-319-23117-4_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23116-7

  • Online ISBN: 978-3-319-23117-4

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