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The death of a relationship


Ansari, A; Koenigsberg, O; Stahl, F (2009). The death of a relationship. In: INFORMS Marketing Science 2009, Ann Arbor, 4 June 2009 - 6 June 2009, 1-2.

Abstract

Users in online social networks ostensibly have relationships with a large number of other users. This has prompted many to comment that the nature of friendship in the online world is different from the offline world. However, even though a user may connect with many others, a majority of such connections become inactive after a period of time, and therefore, many relationships cease to exist. A challenge in modeling such connections is that death is unobserved, as the connection remains, but is not active. As a consequence, models that ignore the death of relationships overestimate the density of a network and can bias measures of user influence. We model interactivity among users in an online social network and explicitly account for unobserved relationship death via extensions of the well known Pareto-NBD and BG-NBD models to social network settings. We estimate our model on a network of musicians using hierarchical Bayesian methods and find that accounting for the death of relationships is beneficial for predicting future interactivity.

Abstract

Users in online social networks ostensibly have relationships with a large number of other users. This has prompted many to comment that the nature of friendship in the online world is different from the offline world. However, even though a user may connect with many others, a majority of such connections become inactive after a period of time, and therefore, many relationships cease to exist. A challenge in modeling such connections is that death is unobserved, as the connection remains, but is not active. As a consequence, models that ignore the death of relationships overestimate the density of a network and can bias measures of user influence. We model interactivity among users in an online social network and explicitly account for unobserved relationship death via extensions of the well known Pareto-NBD and BG-NBD models to social network settings. We estimate our model on a network of musicians using hierarchical Bayesian methods and find that accounting for the death of relationships is beneficial for predicting future interactivity.

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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:6 June 2009
Deposited On:11 Feb 2010 01:35
Last Modified:11 Aug 2017 11:06
Official URL:http://www.bus.umich.edu/mks2009/

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