Joint modelling of multiple network wiews

Article English OPEN
Gollini, Isabella ; Murphy, T.B. (2016)

Latent space models (LSM) for network data were introduced by Hoff et al. (2002) under the basic assumption that each node of the network has an unknown position in a D-dimensional Euclidean latent space: generally the smaller the distance between two nodes in the latent space, the greater their probability of being connected. In this paper we propose a variational inference approach to estimate the intractable posterior of the LSM. In many cases, different network views on the same set of nodes are available. It can therefore be useful to build a model able to jointly summarise the information given by all the network views. For this purpose, we introduce the latent space joint model (LSJM) that merges the information given by multiple network views assuming that the probability of a node being connected with other nodes in each network view is explained by a unique latent variable. This model is demonstrated on the analysis of two datasets: an excerpt of 50 girls from 'Teenage Friends and Lifestyle Study' data at three time points and the Saccharomyces cerevisiae genetic and physical protein-protein interactions.
  • References (17)
    17 references, page 1 of 2

    Airoldi, E. M., Blei, D. M., Fienberg, S. E., and Xing, E. P. (2008), “Mixed-membership Stochastic Blockmodels,” Journal of Machine Learning Research, 9, 1981-2014.

    Ansari, A., Koenigsberg, O., and Stahl, F. (2011), “Modeling multiple relationships in social networks,” Journal of Marketing Research, 48, 713 -728.

    Attias, H. (1999), “Inferring Parameters and Structure of Latent Variable Models by Variational Bayes,” in Proceedings of the Proceedings of the Fifteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-99), San Francisco, CA: Morgan Kaufmann, pp. 21-30.

    Bandyopadhyay, S., Kelley, R., Krogan, N. J., and Ideker, T. (2008), “Functional Maps of Protein Complexes from Quantitative Genetic Interaction Data,” PLoS Computational Biology, 4.

    Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977), “Maximum likelihood for incomplete data via the EM algorithm (with discussion),” Journal of the Royal Statistical Society, Series B, 39, 1-38.

    Goldenberg, A., Zheng, A. X., Fienberg, S. E., and Airoldi, E. M. (2010), “A Survey of Statistical Network Models,” Foundations and Trends in Machine Learning, 2, 129-233.

    Handcock, M., Raftery, A., and Tantrum, J. (2007), “Model-Based Clustering for Social Networks (with discussion),” Journal of the Royal Statistical Society: Series A, 170, 1-22.

    Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., and Saul, L. K. (1999), “An Introduction to Variational Methods for Graphical Models,” Machine Learning, 37, 183-233.

    Koskinen, J. H., Robins, G. L., and Pattison, P. E. (2010), “Analysing exponential random graph (p-star) models with missing data using Bayesian data augmentation,” Statistical Methodology, 7, 366 - 384.

    Koskinen, J. H., Robins, G. L., Wang, P., and Pattison, P. E. (2013), “Bayesian analysis for partially observed network data, missing ties, attributes and actors,” Social Networks, 35, 514 - 527.

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