Discovering Leaders from Community Actions
Source:
Conference on Information and Knowledge Management (CIKM), ACM Press (2008)
Abstract:
We introduce a novel frequent pattern mining approach to
discover leaders and tribes in social networks. In particular,
we consider social networks where users perform actions.
Actions may be as simple as tagging resources (urls) as in
del.icio.us, rating songs as in Yahoo! Music, or movies as
in Yahoo! Movies, or users buying gadgets such as cameras,
handhelds, etc. and blogging a review on the gadgets.
The assumption is that actions performed by a user can be
seen by their network friends. Users seeing their friends’ actions
are sometimes tempted to perform those actions. We
are interested in the problem of studying the propagation of
such “influence”, and on this basis, identifying which users
are leaders when it comes to setting the trend for performing
various actions. We consider alternative definitions of
leaders based on frequent patterns and develop algorithms
for their efficient discovery. Our definitions are based on
observing the way influence propagates in a time window,
as the window is moved in time. Given a social graph and
a table of user actions, our algorithms can discover leaders
of various flavors by making one pass over the actions table.
We run detailed experiments to evaluate the utility and
scalability of our algorithms on real-life data. The results
of our experiments confirm on the one hand, the efficiency
of the proposed algorithm, and on the other hand, the effectiveness
and relevance of the overall framework. To the
best of our knowledge, this the first frequent pattern based
approach to social network mining.
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