Mining Ranking Models from Dynamic Network Data

  • Lucrezia Macchia
  • Michelangelo Ceci
  • Donato Malerba
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7376)


In recent years, improvement in ubiquitous technologies and sensor networks have motivated the application of data mining techniques to network organized data. Network data describe entities represented by nodes, which may be connected with (related to) each other by edges. Many network datasets are characterized by a form of autocorrelation where the value of a variable at a given node depends on the values of variables at the nodes it is connected with. This phenomenon is a direct violation of the assumption that data are independently and identically distributed (i.i.d.). At the same time, it offers the unique opportunity to improve the performance of predictive models on network data, as inferences about one entity can be used to improve inferences about related entities. In this work, we propose a method for learning to rank from network data when data distribution may change over time. The learned models can be used to predict the ranking of nodes in the network for new time periods. The proposed method modifies the SVMRank algorithm in order to emphasize the importance of models learned in time periods during which data follow a data distribution that is similar to that observed in the new time period. We evaluate our approach on several real world problems of learning to rank from network data, coming from the area of sensor networks.


Sensor Network Network Data Ranking Function Neural Information Processing System Concept Drift 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aiolli, F.: A preference model for structured supervised learning tasks. In: ICDM, pp. 557–560. IEEE Computer Society (2005)Google Scholar
  2. 2.
    Ceci, M., Appice, A., Loglisci, C., Malerba, D.: Complex objects ranking: a relational data mining approach. In: Shin, S.Y., Ossowski, S., Schumacher, M., Palakal, M.J., Hung, C.C. (eds.) SAC, pp.1071–1077. ACM (2010)Google Scholar
  3. 3.
    Cohen, W.W., Schapire, R.E., Singer, Y.: Learning to order things. J. Artif. Int. Res. 10, 243–270 (1999)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Crammer, K., Singer, Y.: Pranking with ranking. In: NIPS, pp. 641–647. MIT Press (2001)Google Scholar
  5. 5.
    Dembczyski, K., Kotlowski, W., Slowiski, R., Szelag, M.: Learning of rule ensembles for multiple attribute ranking problems. In: Fürnkranz, J., Hüllermeier, E. (eds.) Preference Learning, pp. 217–247. Springer (2010)Google Scholar
  6. 6.
    Doyle, J.: Prospects for preferences. Computational Intelligence 20(2), 111–136 (2004)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Draper, N., Smith, H.: Applied Regression Analysis, 2nd edn. Wiley, New York (1981)zbMATHGoogle Scholar
  8. 8.
    Fürnkranz, J., Hüllermeier, E.: Pairwise Preference Learning and Ranking. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) ECML 2003. LNCS (LNAI), vol. 2837, pp. 145–156. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  9. 9.
    Har-Peled, S., Roth, D., Zimak, D.: Constraint Classification: A New Approach to Multiclass Classification. In: Cesa-Bianchi, N., Numao, M., Reischuk, R. (eds.) ALT 2002. LNCS (LNAI), vol. 2533, pp. 365–379. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  10. 10.
    Har-Peled, S., Roth, D., Zimak, D.: Constraint classification for multiclass classification and ranking. In: Becker, S., Thrun, S., Obermayer, K. (eds.) Advances in Neural Information Processing Systems 15 (NIPS 2002), pp. 785–792 (2003)Google Scholar
  11. 11.
    Herbrich, R., Graepel, T., Bollmann-sdorra, P., Obermayer, K.: Learning preference relations for information retrieval (1998)Google Scholar
  12. 12.
    Herbrich, R., Graepel, T., Obermayer, K.: Large margin rank boundaries for ordinal regression. MIT Press (2000)Google Scholar
  13. 13.
    Hüllermeier, E., Fürnkranz, J., Cheng, W., Brinker, K.: Label ranking by learning pairwise preferences. Artif. Intell. 172(16-17), 1897–1916 (2008)zbMATHCrossRefGoogle Scholar
  14. 14.
    Jensen, D., Neville, J.: Linkage and autocorrelation cause feature selection bias in relational learning. In: Proc. 9th Intl. Conf. on Machine Learning, pp. 259–266. Morgan Kaufmann (2002)Google Scholar
  15. 15.
    Joachims, T.: Optimizing search engines using clickthrough data. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002, pp. 133–142. ACM, New York (2002)CrossRefGoogle Scholar
  16. 16.
    Malerba, D., Ceci, M.: Learning to Order: A Relational Approach. In: Raś, Z.W., Tsumoto, S., Zighed, D.A. (eds.) MCD 2007. LNCS (LNAI), vol. 4944, pp. 209–223. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  17. 17.
    Neville, J., Simsek, O., Jensen, D.: Autocorrelation and relational learning: Challenges and opportunities. In: Wshp. Statistical Relational Learning (2004)Google Scholar
  18. 18.
    Newman, M.E.J., Watts, D.J.: The structure and dynamics of networks. Princeton University Press (2006)Google Scholar
  19. 19.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical Report 1999-66, Stanford InfoLab (November 1999),, previous number = SIDL-WP-1999-0120
  20. 20.
    Stojanova, D., Ceci, M., Appice, A., Džeroski, S.: Network Regression with Predictive Clustering Trees. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011. LNCS, vol. 6913, pp. 333–348. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  21. 21.
    Swanson, B.J.: Autocorrelated rates of change in animal populations and their relationship to precipitation. Conservation Biology 12(4), 801–808 (1998)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Tesauro, G.: Connectionist learning of expert preferences by comparison training. In: Advances in Neural Information Processing Systems 1, pp. 99–106. Morgan Kaufmann Publishers Inc., San Francisco (1989)Google Scholar
  23. 23.
    Vapnik, V., Golowich, S.E., Smola, A.: Support vector method for function approximation, regression estimation, and signal processing. In: Advances in Neural Information Processing Systems 9, pp. 281–287. MIT Press (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lucrezia Macchia
    • 1
  • Michelangelo Ceci
    • 1
  • Donato Malerba
    • 1
  1. 1.Dipartimento di InformaticaUniversità degli Studi di BariBariItaly

Personalised recommendations