Finding Influencers in Temporal Social Networks Using Intervention Analysis

  • Maximilian Franzke
  • Janina Bleicher
  • Andreas Züfle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9877)


People influence, inspire and learn from each other. The result of such a latent cooperation can be observed in social networks, where interacting users are connected with each other. In previous work, the influence casted by a single individual is measured purely quantitatively, by analyzing the topology of a social network. In this work, we take a step towards analyzing the quality of an influencer. For this purpose, we analyze how attributes describing an individual change over time, and put this change in the context of the social network topology changing over time. Each social node is associated with time-series of their attribute values, such as their number of citations. For each individual, we apply the concept of intervention analysis, to identify points in time, so-called interventions, when these attributes have been affected significantly. Such interventions can be of either positive or negative type, and either short-term or long-term. For each intervention, we use the temporal social network’s topology to identify candidate individuals, potentially responsible for this intervention. We use these interventions to score their influence on others. This allows us to find users, who have a significant bias towards affecting the performance of other users, in a positive or negative way. We evaluate our solution using a data set obtained from the ACM digital library – containing both a temporal collaboration network, as well as time-dependent attributes such as the number of citations. Our resulting most-positively influential researches is quite different from traditional purely quantitative metrics such as citation count and h-index.


Social Network Performance Function Intervention Analysis Citation Count Citation Network 
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.


  1. 1.
    Abraham, B.: Intervention analysis and multiple time series. Biometrika 67(1), 73–78 (1980)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Armenatzoglou, N., Ahuja, R., Papadias, D.: Geo-social ranking: functions and query processing. VLDB J. - Int. J. Very Large Data Bases 24(6), 783–799 (2015)CrossRefGoogle Scholar
  3. 3.
    Armenatzoglou, N., Papadopoulos, S., Papadias, D.: A general framework for geo-social query processing. Proc. VLDB Endowment 6(10), 913–924 (2013)CrossRefGoogle Scholar
  4. 4.
    Bandura, A., McClelland, D.C.: Social learning theory (1977)Google Scholar
  5. 5.
    Bandura, A., Walters, R.H.: Social learning and personality development, vol. 14, JSTOR (1963)Google Scholar
  6. 6.
    Box, G.E., Tiao, G.C.: Intervention analysis with applications to economic and environmental problems. J. Am. Stat. Assoc. 70(349), 70–79 (1975)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Chen, W., Lakshmanan, L.V., Castillo, C.: Information and influence propagation in social networks. Synth. Lect. Data Manage. 5(4), 1–177 (2013)CrossRefGoogle Scholar
  8. 8.
    Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1029–1038. ACM (2010)Google Scholar
  9. 9.
    Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 57–66. ACM (2001)Google Scholar
  10. 10.
    Fleming, N.S., Gibson, E., Fleming, D.G.: The use of proc arima to test an intervention effect. In: SAS Conference Proceedings: South-Central SAS Users Group, pp. 91–98 (1997)Google Scholar
  11. 11.
    Goyal, A., Bonchi, F., Lakshmanan, L.V.: Learning influence probabilities in social networks. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 241–250. ACM (2010)Google Scholar
  12. 12.
    Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. ACM (2003)Google Scholar
  13. 13.
    Khandker, S.R., Koolwal, G.B., Samad, H.A.: Handbook on Impact Evaluation: Quantitative Methods and Practices. World Bank Publications, Washington, D.C (2010)Google Scholar
  14. 14.
    Miller, N.E., Dollard, J.: Social learning and imitation (1941)Google Scholar
  15. 15.
    Pham, H., Shahabi, C.: Spatial influence - measuring followship in the real world. In: IEEE 32nd International Conference on Data Engineering (ICDE), pp. 529–540. IEEE (2016)Google Scholar
  16. 16.
    Reed, M., Evely, A.C., Cundill, G., Fazey, I.R.A., Glass, J., Laing, A., Newig, J., Parrish, B., Prell, C., Raymond, C., Stringer, L.: What is social learning? Ecology and Society (2010)Google Scholar
  17. 17.
    Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 61–70. ACM (2002)Google Scholar
  18. 18.
    Sowell, F.: Maximum likelihood estimation of stationary univariate fractionally integrated time series models. J. Econometrics 53(1), 165–188 (1992)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Maximilian Franzke
    • 1
  • Janina Bleicher
    • 1
  • Andreas Züfle
    • 2
  1. 1.Ludwig-Maximilians-Universität MünchenMünchenGermany
  2. 2.George Mason UniversityFairfaxUSA

Personalised recommendations