Modeling multiple relationships in social networks

Abstract

Firms are increasingly seeking to harness the potential of social networks for marketing purposes. Marketers are therefore interested in understanding the antecedents and consequences of relationship formation within networks and in predicting interactivity among users. In this paper we develop an integrated statistical framework for simultaneously modeling the connectivity structure of multiple relationships of different types on a common set of actors. Our modeling approach incorporates a number of distinct facets to capture both the determinants of relationships and the structural characteristics of multiplex and sequential networks. We develop hierarchical Bayesian methods for estimation and illustrate our model via two applications. The first application uses a sequential network of communications among managers involved in new product development activities and the second uses an online collaborative social network of musicians. Our applications demonstrate the benefits of modeling multiple relations jointly for both substantive and predictive purposes. We also illustrate how information in one relationship can be leveraged to predict connectivity in another relation.