title: Testing and Modeling Dependencies Between a Network and Nodal Attributes.
abstract: Network analysis is often focused on characterizing the dependencies between network relations and node-level attributes. Potential relationships are typically explored by modeling the network as a function of the nodal attributes or by modeling the attributes as a function of the network. These methods require specification of the exact nature of the association between the network and attributes, reduce the network data to a small number of summary statistics, and are unable provide predictions simultaneously for missing attribute and network information. Existing methods that model the attributes and network jointly also assume the data are fully observed. In this article we introduce a unified approach to analysis that addresses these shortcomings. We use a previously developed latent variable model to obtain a low dimensional representation of the network in terms of node-specific network factors. We introduce a novel testing procedure to determine if dependencies exist between the network factors and attributes as a surrogate for a test of dependence between the network and attributes. We also present a joint model for the network relations and attributes, for use if the hypothesis of independence is rejected, that can capture a variety of dependence patterns and be used to make inference and predictions for missing observations.
authors: Department of Statistics, Colorado State University.
authors: Bailey K
authors: BK
authors: Fosdick
authors: Department of Statistics and Biostatistics, University of Washington.
authors: Peter D
authors: PD
authors: Hoff
keywords: hypothesis test; joint model; latent variable model; prediction; relational data