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2014 | 2 | 75--83
Tytuł artykułu

The inverse infection problem

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The applications of infection models like the Linear Threshold or the Domingos-Richardson model requires a graph weighted with infection probabilities. In many real-life applications these probabilities are unknown; therefore a systematic method for the estimation of these probabilities is required. One of the methods proposed to solve this problem, the Inverse Infection Model, was originally formulated for estimating credit default in banking applications. In this paper we are going to test the capabilities of the Inverse Infection Model in a more controlled environment. We are going to use artificially created graphs to evaluate the speed and the accuracy of estimations. We are also going to examine how approximations and heuristics can be used to improve the speed of the calculations. Finally, we will experiment with the amount of a priori information available in the model and evaluate how well this method performs if only partial information is available.(original abstract)
Rocznik
Tom
2
Strony
75--83
Opis fizyczny
Twórcy
  • University of Szeged, Hungary
  • University of Szeged, Hungary
  • University of Szeged, Hungary
Bibliografia
  • Bóta A., Krész M. and Pluhár A., Applications of the Inverse Infection Problem on bank transaction networks. Submitted.
  • Bóta A., Krész M. and Pluhár A., Approximations of the Generalized Cascade Model. Acta Cybernetica 21 (2013) 37-51.
  • Bóta A., Krész M. and Pluhár A., Systematic learning of edge probabilities in the Domingos-Richardson model. Int. J. Complex Systems in Science Volume 1(2) (2011) 115-118.
  • Cao Tianyu, Wu Xindong, Hu Tony Xiaohua and Wang Song, Active Learning of Model Parameters for Influence Maximization. Machine Learning and Knowledge Discovery in Databases, Lecture Notes in Computer Science, eds. Gunopulos et al., Springer Berlin/Heidelberg, (2011) 280-295, http://dx.doi.org/10.1007/978-3-642-23780-5_28.
  • Chen Wei, Wang Chi and Wang Yajun, Scalable Influence Maximization for Prevalent Viral Marketing in Large-Scale Social Networks. Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM (2010) 1029-1038, http://doi.acm.org/10.1145/1835804.1835934.
  • Chen Wei, Yuan Yifei and Zhang Li, Scalable Influence Maximization in Social Networks under the Linear Threshold Model. Proceeding ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining, IEEE Computer Society (2010) 88-97, http://dx.doi.org/10.1109/ICDM.2010.118.
  • Csernenszky A., Kovács Gy., Krész M., Pluhár A., Tóth T., The use of infection models in accounting and crediting. Challenges for Analysis of the Economy, the Businesses, and Social Progress Szeged (2009) pp. 617-623.
  • DeGroot M. H., Reaching a Consensus. Journal of the American Statistical Association, 69 (345): 118-21, http://www.tandfonline.com/doi/pdf/10.1080/01621459.1974.10480137.
  • Diekmann O., Heesterbeek J. A. P., Mathematical epidemiology of infectious diseases. Model Building, Analysis and Interpretation. John Wiley & Sons, 2000.
  • Domingos P., Richardson M., Mining the Network Value of Costumers. Proceedings of the 7th International Conference on Knowledge Discovery and Data Mining, ACM (2001) 57-66, http://doi.acm.org/10.1145/502512.502525.
  • Goyal A., Bonchi F., Lakshmanan L.V.S., Learning influence probabilities in social networks. Proceedings of the third ACM International Conference on Web search and data mining. ACM (2010) 241-250, http://doi.acm.org/10.1145/1718487.1718518.
  • Granovetter M., Threshold models of collective behavior. American Journal of Sociology 83(6) (1978) 1420-1443, http://psycnet.apa.org/doi/10.1086/226707.
  • Kempe D., Kleinberg J., Tardos E., Influential Nodes in a Diffusion Model for Social Networks. Proceedings of the 32nd International Colloquium on Automata, Languages and Programming (ICALP), Springer-Verlag (2005) 1127-1138, http://dx.doi.org/10.1007/11523468_91.
  • Kempe D., Kleinberg J., Tardos E., Maximizing the Spread of Influence though a Social Network. Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM (2003) 137-146, http://doi.acm.org/10.1145/956750.956769.
  • Kennedy J., Mendes R., Neighborhood topologies in fully informed and best-of-neighborhood particle swarms. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews. 36 (4) (2006) 515-519, http://dx.doi.org/10.1109/TSMCC.2006.875410.
  • Kennedy J., Particle Swarm Optimization. Encyclopedia of Machine Learning, Springer US (2010), 760-766, http://dx.doi.org/10.1007/978-0-387-30164-8_630.
  • Kimura M., Saito K., Tractable models for information diffusion in social networks. Knowledge Discovery in Databases, Lecture Notes in Computer Science Springer Berlin / Heidelberg, (2006), 259-271, http://dx.doi.org/10.1007/11871637_27.
  • Leskovec J., Kleinberg J., Faloutsos C., Graphs over time: densification laws, shrinking diameters and possible explanations. Proceedings of the1th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM (2005) 177-187, http://doi.acm.org/10.1145/1081870.1081893.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171320809

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