Tan, LSL;
De Iorio, M;
(2019)
Dynamic degree-corrected blockmodels for social networks: A nonparametric approach.
Statistical Modelling
, 19
(4)
pp. 386-411.
10.1177/1471082X18770760.
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Abstract
A nonparametric approach to the modelling of social networks using degree-corrected stochastic blockmodels is proposed. The model for static network consists of a stochastic blockmodel using a probit regression formulation, and popularity parameters are incorporated to account for degree heterogeneity. We specify a Dirichlet process prior to detect community structure as well as to induce clustering in the popularity parameters. This approach is flexible yet parsimonious as it allows the appropriate number of communities and popularity clusters to be determined automatically by the data. We further discuss and implement extensions of the static model to dynamic networks. In a Bayesian framework, we perform posterior inference through MCMC algorithms. The models are illustrated using several real-world benchmark social networks.
| Type: | Article |
|---|---|
| Title: | Dynamic degree-corrected blockmodels for social networks: A nonparametric approach |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1177/1471082X18770760 |
| Publisher version: | https://doi.org/10.1177/1471082X18770760 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Community detection, degree correction, Dirichlet process, stochastic blockmodels |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10090085 |
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