New Generation Computing

, Volume 32, Issue 3–4, pp 213–235 | Cite as

Seed Selection for Spread of Influence in Social Networks: Temporal vs. Static Approach

  • Radosław Michalski
  • Tomasz Kajdanowicz
  • Piotr Bródka
  • Przemysław Kazienko
Article

Abstract

The problem of finding optimal set of users for influencing others in the social network has been widely studied. Because it is NP-hard, some heuristics were proposed to find sub-optimal solutions. Still, one of the commonly used assumption is the one that seeds are chosen on the static network, not the dynamic one. This static approach is in fact far from the real-world networks, where new nodes may appear and old ones dynamically disappear in course of time.

The main purpose of this paper is to analyse how the results of one of the typical models for spread of influence - linear threshold - differ depending on the strategy of building the social network used later for choosing seeds. To show the impact of network creation strategy on the final number of influenced nodes - outcome of spread of influence, the results for three approaches were studied: one static and two temporal with different granularities, i.e. various number of time windows. Social networks for each time window encapsulated dynamic changes in the network structure. Calculation of various node structural measures like degree or betweenness respected these changes by means of forgetting mechanism - more recent data had greater influence on node measure values. These measures were, in turn, used for node ranking and their selection for seeding.

All concepts were applied to experimental verification on five real datasets. The results revealed that temporal approach is always better than static and the higher granularity in the temporal social network while seeding, the more finally influenced nodes. Additionally, outdegree measure with exponential forgetting typically outperformed other time-dependent structural measures, if used for seed candidate ranking.

Keywords

Social Networks Complex Networks Spread of Influence Seeding Strategies Seed Ranking Node Selection Temporal Networks Temporal Complex Networks Temporal Granularity Network Measures 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bergmann, B. and Gerhard H., “Improvements of general multiple test procedures for redundant systems of hypotheses,” Multiple Hypothesenprfung/Multiple Hypotheses Testing, Springer Berlin Heidelberg, pp. 100–115, 1988.Google Scholar
  2. 2.
    Chen, W. and Yuan, Y. and Zhang, L., “Scalable influence maximization in social networks under the linear threshold model,” in Proc. of 2010 IEEE 10th International Conference on Data Mining, IEEE Computer Society, pp. 88–97, 2010.Google Scholar
  3. 3.
    Choudhury, M. and Sundaram, H. and John, A. and Seligmann, D.D., “Social Synchrony: Predicting Mimicry of User Actions in Online Social Media,” in Proc. Int. Conf. on Computational Science and Engineering, pp. 151–158, 2009.Google Scholar
  4. 4.
    Cliffor P., Sudbury A.: “A model for spatial conflict,”. Biometrika 60(3), 581–588 (1973)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Csardi, G. and Nepusz, T., “The igraph software package for complex network research,” InterJournal, vol. Complex Systems, 2006.Google Scholar
  6. 6.
    Even-Dar, E. and Shapira, A., “A note on maximizing the spread of influence in social networks,” Network (Deng, X. and Graham, F. eds.), 111, 4, ch. 27, pp. 281–286, 2007.Google Scholar
  7. 7.
    Freeman L.C.: “Set of Measures of Centrality Based on Betweenness,”. Sociometry 40(1), 35–41 (1977)CrossRefGoogle Scholar
  8. 8.
    Friedman M.: “The use of ranks to avoid the assumption of normality implicit in the analysis of variance,”. Journal of the American Statistical Association 32(200), 675–701 (1937)CrossRefGoogle Scholar
  9. 9.
    Goldenberg J., Libai B., Muller E.: “Talk of the network: A complex systems look at the underlying process of word-of-mouth,”. Marketing letters 12(3), 211–223 (2001)CrossRefGoogle Scholar
  10. 10.
    Gomez-Rodriguez, M. and Leskovec, J. and Krause, A., “Inferring Networks of Diffusion and Influence,” in Proc. of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining KDD 10, 5, 4, IEEE Computer Society, pp. 1019–1028, 2010.Google Scholar
  11. 11.
    Goyal A., Bonchi F., Lakshmanan L.V.S.: “A data-based approach to social influence maximization,”. In Proc. of the VLDB Endowment 5(1), 73–84 (2011)CrossRefGoogle Scholar
  12. 12.
    Goyal A., Bonchi F., Lakshmanan L.V.S., Venkatasubramanian S.: “On minimizing budget and time in influence propagation over social networks,”. Social Network Analysis and Mining 3(2), 179–192 (2013)CrossRefGoogle Scholar
  13. 13.
    Goyal, A. and Lu, W. and Lakshmanan, L.V.S. “SIMPATH: An Efficient Algorithm for Influence Maximization under the Linear Threshold Model,” in Proc. of 11th IEEE International Conference on Data Mining, IEEE Computer Society, pp. 211–220, 2011.Google Scholar
  14. 14.
    Granovetter M.: “Threshold Models of Collective Behavior,”. American Journal of Sociology 83(6), 1420–1443 (1978)CrossRefGoogle Scholar
  15. 15.
    Holm, S., “A simple sequentially rejective multiple test procedure,” Scandinavian journal of statistics, pp. 65–70, 1969.Google Scholar
  16. 16.
    Holme, P. and Saramäki, J., “Temporal networks,” Physics reports (Deng, X. and Graham, F. eds.), 519, 3, pp. 97–125, 2012.Google Scholar
  17. 17.
    Jankowski, J. and Michalski, R. and Kazienko, P., “Compensatory Seeding in Networks with Varying Availability of Nodes,” in Proc. of 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining ASONAM 2013, pp. 1242–1249, 2013.Google Scholar
  18. 18.
    Karimi F., Holme P.: “Threshold model of cascades in empirical temporal networks,”. Physica A: Statistical Mechanics and its Applications 392, 3476–3483 (2013)CrossRefGoogle Scholar
  19. 19.
    Karsai, M. and Kivelä, M. and Pan, R.K. and Kaski, K. and Kertész, J. and Barabási, A-L. and Saramäki, J., “Small but slow world: How network topology and burstiness slow down spreading,” Physical Review E, 83, 2, 2011.Google Scholar
  20. 20.
    Kazienko P., Kajdanowicz T.: “Label-dependent node classification in the network,”. Neurocomputing 75, 199–209 (2012)CrossRefGoogle Scholar
  21. 21.
    Kempe, D. and Kleinberg, J. and Tardos, E., “Maximizing the spread of influence through a social network,” in Proc. of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 2003, ACM Press, pp. 137–146, 2003.Google Scholar
  22. 22.
    Klimt, B. and Yang, Y., “The enron corpus: A new dataset for email classification research,” in Proc. of ECML 2004 - European Conference on Machine learning, Springer, pp. 217–226, 2004.Google Scholar
  23. 23.
    Kossinets, G. and Watts, D.J., “Empirical analysis of an evolving social network,” Science (Deng, X. and Graham, F. eds.), 311, 5757, pp. 88–90, 2006.Google Scholar
  24. 24.
    Król, D., “On Modelling Social Propagation Phenomenon,” LNCS, 8398, Springer Verlag, pp. 227–236, 2014.Google Scholar
  25. 25.
    Liben-Nowell D., Kleinberg J.: “The link prediction problem for social networks,”. Journal of the American society for information science and technology 58(7), 1019–1031 (2007)CrossRefGoogle Scholar
  26. 26.
    Masuda, N. and Holme, P., “Predicting and controlling infectious disease epidemics using temporal networks,” F1000prime reports, 5, 2013.Google Scholar
  27. 27.
    Mathioudakis, M. and Bonchi, F. and Castillo, C. and Gionis, A. and Ukkonen, A., “Sparsification of influence networks,” in Proc. of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM Press, pp. 529–537, 2011.Google Scholar
  28. 28.
    Michalski, R. and Bródka, P. and Kazienko, P. and Juszczyszyn, K., “Quantifying social network dynamics,” in Proc. of the 4th Conference on Computational Aspects of Social Networks (CASoN), IEEE Computer Society, pp. 69–74, 2012.Google Scholar
  29. 29.
    Michalski, R. and Kazienko, P. and Jankowski, J., “Convince a Dozen More and Succeed–The Influence in Multi-layered Social Networks,” in Proc. of the International Conference on Signal-Image Technology & Internet-Based Systems (SITIS 2013), IEEE Computer Society, pp. 499–505, 2013.Google Scholar
  30. 30.
    Michalski R., Palus S., Kazienko P.: “Matching Organizational Structure and Social Network Extracted from Email Communication,”. Lecture Notes in Business Information Processing 87, 197–206 (2011)CrossRefGoogle Scholar
  31. 31.
    Moon, J.W. and Moser, L., “On cliques in graphs,” Israel journal of Mathematics, pp. 23–28, 1965.Google Scholar
  32. 32.
    Nemenyi, P., “Distribution-free multiple comparisons,” Dissemination at Princeton University, 1963.Google Scholar
  33. 33.
    Opsahl T., Panzarasa P.: “Clustering in Weighted Networks,”. Social Networks 31(2), 155–163 (2009)CrossRefGoogle Scholar
  34. 34.
    Palla, G, and Barabsi, A-L. and Vicsek, T., “Quantifying social group evolution,” Nature, 2007.Google Scholar
  35. 35.
    Prell, C., Social network analysis: History, theory and methodology, Sage Publications Limited, 2011.Google Scholar
  36. 36.
    R Development Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, 2011.Google Scholar
  37. 37.
    Rogers, E.M., Diffusion of innovations, Simon and Schuster, 2010.Google Scholar
  38. 38.
    Saito, K. and Nakano, R. and Kimura, M. and Lovrek, I. and Howlett, R. and Jain, L., “Prediction of Information Diffusion Probabilities for Independent Cascade Model,” in KnowledgeBased Intelligent Information and Engineering Systems (Lovrek, I. and Howlett, R.J. and Jain, L. eds.), Springer Verlag, pp. 67–75, 2008.Google Scholar
  39. 39.
    Shaffer : “Multiple hypothesis testing,”. Annual review of psychology 46(1), 561–584 (1995)CrossRefGoogle Scholar
  40. 40.
    Spira P.M., Pan A.: “On finding and updating spanning trees and shortest paths,”. IAM Journal on Computing 4(3), 375–380 (1975)MathSciNetMATHGoogle Scholar
  41. 41.
    Viswanath, B. and Mislove, A. and Cha, M. and Gummadi, K.P., “On the Evolution of User Interaction in Facebook,” in Proc. Workshop on Online Social Networks, Springer, pp. 37–42, 2009.Google Scholar

Copyright information

© Ohmsha and Springer Japan 2014

Authors and Affiliations

  • Radosław Michalski
    • 1
  • Tomasz Kajdanowicz
    • 1
  • Piotr Bródka
    • 1
  • Przemysław Kazienko
    • 1
  1. 1.Institute of InformaticsWrocław University of Technology, WybrzeżeWrocławPoland

Personalised recommendations