NDLI: Bimodal invitation-navigation fair bets model for authority identification in a social network

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Bimodal invitation-navigation fair bets model for authority identification in a social network

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Bimodal invitation-navigation fair bets model for authority identification in a social network

Content Provider	ACM Digital Library
Author	Budalakoti, Suratna Bekkerman, Ron
Abstract	We consider the problem of identifying the most respected, authoritative members of a large-scale online social network (OSN) by constructing a global ranked list of its members. The problem is distinct from the problem of identifying influencers: we are interested in identifying members who are influential in the real world, even when not necessarily so on the OSN. We focus on two sources for information about user authority: (a) invitations to connect, which are usually sent to people whom the inviter respects, and (b) members' browsing behavior, as profiles of more important people are viewed more often than others'. We construct two directed graphs over the same set of nodes (representing member profiles): the invitation graph and the navigation graph respectively. We show that the standard PageRank algorithm, a baseline in web page ranking, is not effective in people ranking, and develop a social capital based model, called the fair bets model, as a viable solution. We then propose a novel approach, called bimodal fair bets, for combining information from two (or more) endorsement graphs drawn from the same OSN, by simultaneously using the authority scores of nodes in one graph to inform the other, and vice versa, in a mutually reinforcing fashion. We evaluate the ranking results on the LinkedIn social network using this model, where members who have Wikipedia profiles are assumed to be authoritative. Experimental results show that our approach outperforms the baseline approach by a large margin.
Starting Page	709
Ending Page	718
Page Count	10
File Format	PDF
ISBN	9781450312295
DOI	10.1145/2187836.2187932
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-04-16
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Graph analysis Authority Pagerank Social networks
Content Type	Text
Resource Type	Article

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