Discovering the Network Backbone from Traffic Activity Data

  • Sanjay Chawla
  • Kiran Garimella
  • Aristides Gionis
  • Dominic Tsang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9651)

Abstract

We introduce a new computational problem, the BackboneDiscovery problem, which encapsulates both functional and structural aspects of network analysis. While the topology of a typical road network has been available for a long time (e.g., through maps), it is only recently that fine-granularity functional (activity and usage) information about the network (like source-destination traffic information) is being collected and is readily available. The combination of functional and structural information provides an efficient way to explore and understand usage patterns of networks and aid in design and decision making. We propose efficient algorithms for the BackboneDiscovery problem including a novel use of edge centrality. We observe that for many real world networks, our algorithm produces a backbone with a small subset of the edges that support a large percentage of the network activity.

References

  1. 1.
    Boldi, P., Vigna, S.: Axioms for centrality (2013). CoRR abs/1308.2140
  2. 2.
    Bonchi, F., De Francisci Morales, G., Gionis, A., Ukkonen, A.: Activity preserving graph simplification. DMKD 27(3), 321–343 (2013)MathSciNetMATHGoogle Scholar
  3. 3.
    Du, N., Wu, B., Wang, B.: Backbone discovery in social networks. In: Web Intelligence (2007)Google Scholar
  4. 4.
    Hajiaghayi, M.T., Khandekar, R., Kortsarz, G., Nutov, Z.: Prize-collecting steiner network problems. In: Eisenbrand, F., Shepherd, F.B. (eds.) IPCO 2010. LNCS, vol. 6080, pp. 71–84. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Marchiori, M., Latora, V.: Harmony in the small world. Physica A 285, 539 (2000)CrossRefMATHGoogle Scholar
  6. 6.
    Mathioudakis, M., Bonchi, F., Castillo, C., Gionis, A., Ukkonen, A.: Sparsification of influence networks. In: KDD (2011)Google Scholar
  7. 7.
    Misiolek, E., Chen, D.Z.: Two flow network simplification algorithms. IPL 97, 197–202 (2006)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Narasimhan, G., Smid, M.: Geometric Spanner Networks. Cambridge University Press, Cambridge (2007)CrossRefMATHGoogle Scholar
  9. 9.
    Potamias, M., Bonchi, F., Castillo, C., Gionis, A.: Fast shortest path distance estimation in large networks. In: CIKM (2009)Google Scholar
  10. 10.
    Ruan, N., Jin, R., Wang, G., Huang, K.: Network backbone discovery using edge clustering (2012). arxiv:1202.1842
  11. 11.
    Toivonen, H., Mahler, S., Zhou, F.: A framework for path-oriented network simplification. In: Cohen, P.R., Adams, N.M., Berthold, M.R. (eds.) IDA 2010. LNCS, vol. 6065, pp. 220–231. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    West, R., Pineau, J., Precup, D.: Wikispeedia: an online game for inferring semantic distances between concepts. In: IJCAI, pp. 1598–1603 (2009)Google Scholar
  13. 13.
    Williamson, D., Shmoys, D.: The design of approximation algorithms. In: CUP (2011)Google Scholar
  14. 14.
    Zhou, F., Mahler, S., Toivonen, H.: Network simplification with minimal loss of connectivity. In: IDA (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Sanjay Chawla
    • 1
    • 2
  • Kiran Garimella
    • 3
  • Aristides Gionis
    • 3
  • Dominic Tsang
    • 2
  1. 1.Qatar Computing Research InstituteHBKUDohaQatar
  2. 2.University of SydneySydneyAustralia
  3. 3.Aalto UniversityEspooFinland

Personalised recommendations