Advertisement

Efficient Incremental Laplace Centrality Algorithm for Dynamic Networks

  • Rui Portocarrero SarmentoEmail author
  • Mário Cordeiro
  • Pavel Brazdil
  • João Gama
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 689)

Abstract

Social Network Analysis (SNA) is an important research area. It originated in sociology but has spread to other areas of research, including anthropology, biology, information science, organizational studies, political science, and computer science. This has stimulated research on how to support SNA with the development of new algorithms. One of the critical areas involves calculation of different centrality measures. The challenge is how to do this fast, as many increasingly larger datasets are available. Our contribution is an incremental version of the Laplacian Centrality measure that can be applied not only to large graphs but also to dynamically changing networks. We have conducted several tests with different types of evolving networks. We show that our incremental version can process a given large network, faster than the corresponding batch version in both incremental and full dynamic network setups.

Notes

Acknowledgements

This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project \(\ll \)POCI-01-0145-FEDER-006961\(\gg \) , and by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013. Rui Portocarrero Sarmento also gratefully acknowledges funding from FCT (Portuguese Foundation for Science and Technology) through a PhD grant (SFRH/BD/119108/2016)

References

  1. 1.
    Brandes, U.: A faster algorithm for betweenness centrality. J. Math. Soc. 25, 163–177 (2001)CrossRefzbMATHGoogle Scholar
  2. 2.
    Floyd, R.W.: Algorithm 97: Shortest path. Commun. ACM 5(6), 345– (1962).  https://doi.org/10.1145/367766.368168
  3. 3.
    Kas, M., Carley, K.M., Carley, L.R.: Incremental closeness centrality for dynamically changing social networks. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM’13, pp. 1250–1258. ACM, New York, NY, USA (2013).  https://doi.org/10.1145/2492517.2500270
  4. 4.
    Leskovec, J., Kleinberg, J., Faloutsos, C.: Graphs over time: densification laws, shrinking diameters and possible explanations. In: Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining-KDD’05, p. 177. ACM Press, New York, New York, USA (2005).  https://doi.org/10.1145/1081870.1081893
  5. 5.
    Nasre, M., Pontecorvi, M., Ramachandran, V.: Betweenness centrality-incremental and faster. CoRR arXiv:1311.2147 (2013)
  6. 6.
    Qi, X., Duval, R.D., Christensen, K., Fuller, E., Spahiu, A., Wu, Q., Wu, Y., Tang, W., Zhang, C.: Terrorist networks, network energy and node removal: a new measure of centrality based on laplacian energy. Soc. Netw. 02(01), 19–31 (2013).  https://doi.org/10.4236/sn.2013.21003 CrossRefGoogle Scholar
  7. 7.
    Qi, X., Fuller, E., Wu, Q., Wu, Y., Zhang, C.Q.: Laplacian centrality: A new centrality measure for weighted networks. Inf. Sci. 194, 240–253 (2012).  https://doi.org/10.1016/j.ins.2011.12.027
  8. 8.
    Ramalingam, G., Reps, T.: An incremental algorithm for a generalization of the shortest-path problem. J. Algorithms 21(2), 267–305 (1996).  https://doi.org/10.1006/jagm.1996.0046
  9. 9.
    igraph core team, T.: igraph-python recipes. http://igraph.wikidot.com/python-recipes#toc4 (2014). Accessed July 2017
  10. 10.
    Wheeler, A.P.: Laplacian centrality in networkx (python). https://andrewpwheeler.wordpress.com/2015/07/29/laplacian-centrality-in-networkx-python/ (2015). Accessed Apr 2017

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Rui Portocarrero Sarmento
    • 1
    Email author
  • Mário Cordeiro
    • 1
  • Pavel Brazdil
    • 2
  • João Gama
    • 2
  1. 1.PRODEI - FEUPUniversity of PortoPortoPortugal
  2. 2.INESC TEC - LIAADPortoPortugal

Personalised recommendations