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.
Dealing with reciprocity in dynamic stochastic block models / Bartolucci, Francesco; Marino, Maria Francesca; Pandolfi, Silvia. - In: COMPUTATIONAL STATISTICS & DATA ANALYSIS. - ISSN 0167-9473. - ELETTRONICO. - 123:(2018), pp. 86-100. [10.1016/j.csda.2018.01.010]
Dealing with reciprocity in dynamic stochastic block models
Marino, Maria FrancescaMembro del Collaboration Group
;
2018
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.I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.