User menu

Comparison of Graph Node Distances on Clustering Tasks

Bibliographic reference Sommer, Félix ; Fouss, François ; Saerens, Marco. Comparison of Graph Node Distances on Clustering Tasks.25th International Conference on Artificial Neural Networks (ICANN 2016) (Barcelona, Spain, du 06/09/2016 au 09/09/2016). In: Alessandro E.P. Villa, Paolo Masulli, Antonio Javier Pons Rivero, Artificial Neural Networks and Machine Learning, Springer : Switzerland2016, p. 192–201
Permanent URL http://hdl.handle.net/2078.1/187106
  1. Bavaud François, Guex Guillaume, Interpolating between Random Walks and Shortest Paths: A Path Functional Approach, Lecture Notes in Computer Science (2012) ISBN:9783642353857 p.68-81, 10.1007/978-3-642-35386-4_6
  2. Blondel Vincent D, Guillaume Jean-Loup, Lambiotte Renaud, Lefebvre Etienne, Fast unfolding of communities in large networks, 10.1088/1742-5468/2008/10/p10008
  3. Borg Ingwer, Groenen Patrick, Modern Multidimensional Scaling, ISBN:9781475727135, 10.1007/978-1-4757-2711-1
  4. Celeux, G., Diday, E., Govaert, G., Lechevallier, Y., Ralambondrainy, H.: Classification Automatique des Données. Dunod, Paris (1989)
  5. Chebotarev, P., Shamis, E.: The matrix-forest theorem and measuring relations in small social groups. Autom. Remote Control 58(9), 1505–1514 (1997)
  6. Chebotarev Pavel, A class of graph-geodetic distances generalizing the shortest-path and the resistance distances, 10.1016/j.dam.2010.11.017
  7. Chebotarev Pavel, The graph bottleneck identity, 10.1016/j.aam.2010.11.001
  8. Collignon, A., Maes, F., Delaere, D., Vandermeulen, D., Suetens, P., Marchal, G.: Automated multi-modality image registration based on information theory. Inf. Process. Med. Imaging 3, 263–274 (1995)
  9. Cormen, T., Leiserson, C., Rivest, R., Stein, C.: Introduction to Algorithms, 3rd edn. The MIT Press, Cambridge (2009)
  10. Daniel, W.: Applied Nonparametric Statistics. The Duxbury Advanced Series in Statistics and Decision Sciences. PWS-Kent Publications, Boston (1990)
  11. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
  12. Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley, New York (1973)
  13. Fouss, F., Saerens, M., Shimbo, M.: Algorithms for Exploratory Link Analysis. Cambridge University Press (2016, to appear)
  14. Françoisse, K., Kivimäki, I., Mantrach, A., Rossi, F., Saerens, M.: A bag-of-paths framework for network data analysis, pp. 1–36 (2013). arXiv:1302.6766
  15. Girvan M., Newman M. E. J., Community structure in social and biological networks, 10.1073/pnas.122653799
  16. Grady, L., Schwartz, E.: The graph analysis toolbox: image processing on arbitrary graphs. CAS/CNS Technical report Series (021) (2010)
  17. Hashimoto, T., Sun, Y., Jaakkola, T.: From random walks to distances on unweighted graphs. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 28, pp. 3411–3419. Curran Associates, Inc. (2015)
  18. Hubert Lawrence, Arabie Phipps, Comparing partitions, 10.1007/bf01908075
  19. Finding Groups in Data, ISBN:9780470316801, 10.1002/9780470316801
  20. Kivimäki Ilkka, Shimbo Masashi, Saerens Marco, Developments in the theory of randomized shortest paths with a comparison of graph node distances, 10.1016/j.physa.2013.09.016
  21. Krebs, V.: New political patterns (2008). http://www.orgnet.com/divided.html
  22. Lancichinetti Andrea, Fortunato Santo, Radicchi Filippo, Benchmark graphs for testing community detection algorithms, 10.1103/physreve.78.046110
  23. Lang, K.: 20 newsgroups dataset. http://bit.ly/lang-newsgroups
  24. Newman Mark, Networks, ISBN:9780199206650, 10.1093/acprof:oso/9780199206650.001.0001
  25. Newman M. E. J., Finding community structure in networks using the eigenvectors of matrices, 10.1103/physreve.74.036104
  26. Newman M. E. J., Modularity and community structure in networks, 10.1073/pnas.0601602103
  27. Newman M. E. J., Girvan M., Finding and evaluating community structure in networks, 10.1103/physreve.69.026113
  28. Saerens Marco, Achbany Youssef, Fouss François, Yen Luh, Randomized Shortest-Path Problems: Two Related Models, 10.1162/neco.2009.11-07-643
  29. Schölkopf, B., Smola, A.: Learning with Kernels. The MIT Press, Cambridge (2002)
  30. Senelle Mathieu, Garcia-Diez Silvia, Mantrach Amin, Shimbo Masashi, Saerens Marco, Fouss Francois, The Sum-over-Forests Density Index: Identifying Dense Regions in a Graph, 10.1109/tpami.2013.227
  31. Siegel, S.: Nonparametric Statistics for the Behavioral Sciences. McGraw-Hill, New York (1956)
  32. Sommer, F., Fouss, F., Saerens, M.: Clustering using a Sum-Over-Forests weighted kernel k-means approach. LSM Working Paper 22 (2015)
  33. von Luxburg, U., Radl, A., Hein, M.: Getting lost in space: large sample analysis of the commute distance. In: Proceedings of the 23th Neural Information Processing Systems Conference (NIPS 2010), pp. 2622–2630 (2010)
  34. von Luxburg, U., Radl, A., Hein, M.: Hitting and commute times in large random neighborhood graphs. J. Mach. Learn. Res. 15, 1751–1798 (2014)
  35. Yen Luh, Fouss Francois, Decaestecker Christine, Francq Pascal, Saerens Marco, Graph Nodes Clustering Based on the Commute-Time Kernel, Advances in Knowledge Discovery and Data Mining ISBN:9783540717003 p.1037-1045, 10.1007/978-3-540-71701-0_117
  36. Yen Luh, Fouss Francois, Decaestecker Christine, Francq Pascal, Saerens Marco, Graph nodes clustering with the sigmoid commute-time kernel: A comparative study, 10.1016/j.datak.2008.10.006
  37. Yen Luh, Saerens Marco, Mantrach Amin, Shimbo Masashi, A family of dissimilarity measures between nodes generalizing both the shortest-path and the commute-time distances, 10.1145/1401890.1401984
  38. Zachary Wayne W., An Information Flow Model for Conflict and Fission in Small Groups, 10.1086/jar.33.4.3629752