UZH-Logo

Maintenance Infos

Modeling Connection Structure in Online Networks


Ansari, A; Koenigsberg, O; Stahl, F (2009). Modeling Connection Structure in Online Networks. In: The Modeling Social Network Data conference, The Wharton School, University Of Pennsylvania, 27 January 2009 - 29 January 2009, 1-2.

Abstract

Firms are increasingly becoming interested in harnessing the potential of online social networks for marketing purposes. Marketers are therefore interested in understanding the antecedents and consequences of relationship formation within such social networks and in predicting the 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 the same set of individuals. Our modeling approach accommodates both the directionality and intensity of network connections and in particular, we show how sparse network connections can be modeled when dealing with weighted relationships. We develop hierarchical Bayesian methods for inference for the resulting model, and then apply our model to data from an online social network of music artists. In our application we model friendship, communication and music download relationships among these artists. We find that these relationships are impacted by common antecedents and users exhibit similar roles across the three relationships. We also find that it is crucial to model the sparsity of connections so as to recover and predict the macrostructure of the network connections.

Firms are increasingly becoming interested in harnessing the potential of online social networks for marketing purposes. Marketers are therefore interested in understanding the antecedents and consequences of relationship formation within such social networks and in predicting the 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 the same set of individuals. Our modeling approach accommodates both the directionality and intensity of network connections and in particular, we show how sparse network connections can be modeled when dealing with weighted relationships. We develop hierarchical Bayesian methods for inference for the resulting model, and then apply our model to data from an online social network of music artists. In our application we model friendship, communication and music download relationships among these artists. We find that these relationships are impacted by common antecedents and users exhibit similar roles across the three relationships. We also find that it is crucial to model the sparsity of connections so as to recover and predict the macrostructure of the network connections.

Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Business Administration
Dewey Decimal Classification:330 Economics
Language:English
Event End Date:29 January 2009
Deposited On:04 Feb 2010 22:20
Last Modified:05 Apr 2016 13:47

Download

Full text not available from this repository.

TrendTerms

TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.

Author Collaborations