Towards a Framework for Learning from Networked Data

  • Jan RamonEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8577)


Over the past decades, one has seen databases of ever increasing size and complexity. While the increasing size is easy to measure in bytes, kilobytes or terabytes, the increase in complexity is more difficult to quantify, however, it has a very deep effect on the theory we use to reason about the data. While in earlier days many researchers reasoned in terms of sets of similarly structured and independent objects, today we are facing large networks of data where everything is connected directly or indirectly to everything else. Examples include social networks, traffic networks, biological networks, administrative networks and economic networks


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Barabási, A.L.: Scale-free networks: A decade and beyond. Science 325(5939), 412–413 (2009)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Calders, T., Ramon, J., Van Dyck, D.: All normalized anti-monotonic overlap graph measures are bounded. Data Mining and Knowledge Discovery 23, 503–548 (2011)CrossRefzbMATHMathSciNetGoogle Scholar
  3. 3.
    De Raedt, L.: Logical settings for concept learning. Artificial Intelligence 95, 187–201 (1997)CrossRefzbMATHMathSciNetGoogle Scholar
  4. 4.
    De Raedt, L.: Attribute-value learning versus inductive logic programming: The missing links (extended abstract). In: Page, D. (ed.) ILP 1998. LNCS (LNAI), vol. 1446, pp. 1–8. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  5. 5.
    Fannes, T., Vandermarliere, E., Schietgat, L., Degroeve, S., Martens, L., Ramon, J.: Predicting tryptic cleavage from proteomics data using decision tree ensembles. Journal of Proteome Research 12, 2253–2259 (2013)CrossRefGoogle Scholar
  6. 6.
    Getoor, L., Taskar, B.: An Introduction to Statistical Relational Learning. MIT Press (2007)Google Scholar
  7. 7.
    Horváth, T., Ramon, J., Wrobel, S.: Frequent subgraph mining in outerplanar graphs. Knowledge Discovery and Data Mining 21(3), 472–508 (2010)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Kibriya, A., Ramon, J.: Nearly exact mining of frequent trees in large networks. Data Mining and Knowledge Discovery 27, 478–504 (2013)CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Martens, L., Laukens, K., Ramon, J., Valkenborg, D.: Inspector: An integrated informatics platform for mass-spectrometry protein assaysGoogle Scholar
  10. 10.
    Newman, M.: Networks: An introduction. Oxford University Press (2010)Google Scholar
  11. 11.
    Nienhuys-Cheng, S.-H., de Wolf, R.: Foundations of Inductive Logic Programming. LNCS (LNAI), vol. 1228. Springer, Heidelberg (1997)Google Scholar
  12. 12.
    Wang, Y., Ramon, J., Guo, Z.-C.: Learning from networked examples in a k-partite graph. In: Proceedings of la Confrence sur l’Apprentissage Automatique, Lille, France, pp. 1–8 (July 2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer ScienceKU LeuvenHeverleeBelgium

Personalised recommendations