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.
Similar content being viewed by others
References
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.
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.
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.
Cliffor P., Sudbury A.: “A model for spatial conflict,”. Biometrika 60(3), 581–588 (1973)
Csardi, G. and Nepusz, T., “The igraph software package for complex network research,” InterJournal, vol. Complex Systems, 2006.
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.
Freeman L.C.: “Set of Measures of Centrality Based on Betweenness,”. Sociometry 40(1), 35–41 (1977)
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)
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)
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.
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)
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)
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.
Granovetter M.: “Threshold Models of Collective Behavior,”. American Journal of Sociology 83(6), 1420–1443 (1978)
Holm, S., “A simple sequentially rejective multiple test procedure,” Scandinavian journal of statistics, pp. 65–70, 1969.
Holme, P. and Saramäki, J., “Temporal networks,” Physics reports (Deng, X. and Graham, F. eds.), 519, 3, pp. 97–125, 2012.
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.
Karimi F., Holme P.: “Threshold model of cascades in empirical temporal networks,”. Physica A: Statistical Mechanics and its Applications 392, 3476–3483 (2013)
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.
Kazienko P., Kajdanowicz T.: “Label-dependent node classification in the network,”. Neurocomputing 75, 199–209 (2012)
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.
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.
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.
Król, D., “On Modelling Social Propagation Phenomenon,” LNCS, 8398, Springer Verlag, pp. 227–236, 2014.
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)
Masuda, N. and Holme, P., “Predicting and controlling infectious disease epidemics using temporal networks,” F1000prime reports, 5, 2013.
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.
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.
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.
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)
Moon, J.W. and Moser, L., “On cliques in graphs,” Israel journal of Mathematics, pp. 23–28, 1965.
Nemenyi, P., “Distribution-free multiple comparisons,” Dissemination at Princeton University, 1963.
Opsahl T., Panzarasa P.: “Clustering in Weighted Networks,”. Social Networks 31(2), 155–163 (2009)
Palla, G, and Barabsi, A-L. and Vicsek, T., “Quantifying social group evolution,” Nature, 2007.
Prell, C., Social network analysis: History, theory and methodology, Sage Publications Limited, 2011.
R Development Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, 2011.
Rogers, E.M., Diffusion of innovations, Simon and Schuster, 2010.
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.
Shaffer : “Multiple hypothesis testing,”. Annual review of psychology 46(1), 561–584 (1995)
Spira P.M., Pan A.: “On finding and updating spanning trees and shortest paths,”. IAM Journal on Computing 4(3), 375–380 (1975)
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.
Author information
Authors and Affiliations
Corresponding author
About this article
Cite this article
Michalski, R., Kajdanowicz, T., Bródka, P. et al. Seed Selection for Spread of Influence in Social Networks: Temporal vs. Static Approach. New Gener. Comput. 32, 213–235 (2014). https://doi.org/10.1007/s00354-014-0402-9
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00354-014-0402-9