Network Inference Using the Hub Model and Variants

TL;DR

This paper proves the identifiability and consistency of the hub model for network inference from group data, introduces a null component for hubless groups, and proposes a penalized likelihood method for hub set estimation.

Contribution

It establishes theoretical guarantees for the hub model, extends it with a null component, and develops a method to estimate the hub set from data.

Findings

Proved identifiability of the hub model parameters.

Established estimation consistency under mild conditions.

Introduced a penalized likelihood approach for hub set estimation.

Abstract

Statistical network analysis primarily focuses on inferring the parameters of an observed network. In many applications, especially in the social sciences, the observed data is the groups formed by individual subjects. In these applications, the network is itself a parameter of a statistical model. Zhao and Weko (2019) propose a model-based approach, called the hub model, to infer implicit networks from grouping behavior. The hub model assumes that each member of the group is brought together by a member of the group called the hub. The set of members which can serve as a hub is called the hub set. The hub model belongs to the family of Bernoulli mixture models. Identifiability of Bernoulli mixture model parameters is a notoriously difficult problem. This paper proves identifiability of the hub model parameters and estimation consistency under mild conditions. Furthermore, this paper generalizes the hub model by introducing a model component that allows hubless groups in which individual nodes spontaneously appear independent of any other individual. We refer to this additional component as the null component. The new model bridges the gap between the hub model and the degenerate case of the mixture model -- the Bernoulli product. Identifiability and consistency are also proved for the new model. In addition, a penalized likelihood approach is proposed to estimate the hub set when it is unknown.