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Dealing with reciprocity in dynamic stochastic block models

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  • Bartolucci, Francesco
  • Marino, Maria Francesca
  • Pandolfi, Silvia

Abstract

A stochastic block model for dynamic network data is introduced, where directed relations among a set of nodes are observed at different time occasions and the blocks are represented by a sequence of latent variables following a Markov chain. Dyads are explicitly modeled conditional on the states occupied by both nodes involved in the relation. With respect to the approaches already available in the literature, the main focus is on reciprocity. In this regard, three different parameterizations are proposed in which: (i) reciprocity is allowed to depend on the blocks of the nodes in the dyad; (ii) reciprocity is assumed to be constant across blocks; and (iii) reciprocity is ruled out. The assumption of conditional independence between dyads (referred to different pairs of nodes and time occasions) given the latent blocks is always retained. Given the complexity of the model, inference on its parameters is based on a variational approach, where a lower bound of the log-likelihood function is maximized instead of the intractable full model log-likelihood. An approximate likelihood ratio test statistic is proposed which compares the value at convergence of this lower bound under different model specifications. This allows us to formally test for both the hypothesis of no reciprocity and that of constant reciprocity with respect to the latent blocks. The proposed approach is illustrated via a simulation study based on different scenarios. The application to two benchmark datasets in the social network literature is also proposed to illustrate the effectiveness of the proposal in studying reciprocity and identifying groups of nodes having a similar social behavior.

Suggested Citation

  • Bartolucci, Francesco & Marino, Maria Francesca & Pandolfi, Silvia, 2018. "Dealing with reciprocity in dynamic stochastic block models," Computational Statistics & Data Analysis, Elsevier, vol. 123(C), pages 86-100.
    • Handle: RePEc:eee:csdana:v:123:y:2018:i:c:p:86-100
    DOI: 10.1016/j.csda.2018.01.010
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    1. N. Lee & C. Priebe, 2011. "A latent process model for time series of attributed random graphs," Statistical Inference for Stochastic Processes, Springer, vol. 14(3), pages 231-253, October.
    2. Daniele Durante & David B. Dunson, 2014. "Nonparametric Bayes dynamic modelling of relational data," Biometrika, Biometrika Trust, vol. 101(4), pages 883-898.
    3. Hoff, Peter D., 2011. "Hierarchical multilinear models for multiway data," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 530-543, January.
    4. Christophe Ambroise & Catherine Matias, 2012. "New consistent and asymptotically normal parameter estimates for random‐graph mixture models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(1), pages 3-35, January.
    5. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    6. Catherine Matias & Vincent Miele, 2017. "Statistical clustering of temporal networks through a dynamic stochastic block model," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1119-1141, September.
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    1. Marino, Maria Francesca & Pandolfi, Silvia, 2022. "Hybrid maximum likelihood inference for stochastic block models," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
    2. Chabert-Liddell, Saint-Clair & Barbillon, Pierre & Donnet, Sophie & Lazega, Emmanuel, 2021. "A stochastic block model approach for the analysis of multilevel networks: An application to the sociology of organizations," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).

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