  In social network analysis, the observed data is usually some social
behavior, such as the formation of groups, rather than an explicit network
structure. Zhao and Weko (2017) propose a model-based approach called the hub
model to infer implicit networks from grouped observations. The hub model
assumes independence between groups, which sometimes is not valid in practice.
In this article, we generalize the idea of the hub model into the case of
grouped observations with temporal dependence. As in the hub model, we assume
that the group at each time point is gathered by one leader. Unlike in the hub
model, the group leaders are not sampled independently but follow a Markov
chain, and other members in adjacent groups can also be correlated.
  An expectation-maximization (EM) algorithm is developed for this model and a
polynomial-time algorithm is proposed for the E-step. The performance of the
new model is evaluated under different simulation settings. We apply this model
to a data set of the Kibale Chimpanzee Project.
