Network Inference from Temporal-Dependent Grouped Observations
This work addresses the limitation of independence assumptions in network inference for social scientists, though it is incremental as it extends an existing model to handle temporal dependencies.
The paper tackles the problem of inferring implicit social networks from temporally dependent grouped observations, generalizing the hub model to account for Markovian dependencies among group leaders and correlations among members. It develops an EM algorithm with a polynomial-time E-step and demonstrates performance through simulations and application to chimpanzee data.
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.