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*Diffusion Centrality in Social Networks*

##### V.S. Subrahmanian, Ph.D.

Professor, Department of Computer
Science

University of Maryland

**Abstract:** Though centrality of vertices in
social networks has been extremely studied, all past efforts assume that
centrality of a vertex solely depends on the structural properties of graphs.
However, with the emergence of online "semantic" social networks where vertices
have properties (e.g., sex, demographics) and edges are labeled with
relationships (e.g., friends, follows) and weights (measuring the strength of a
relationship), it is essential that we take semantics into account when
measuring centrality. Moreover, the centrality of a node should be tied to
diffusive property in the network - a Twitter vertex may have high centrality
with regard to jazz, but low centrality with regard to Republican politics. We
propose a new notion of "diffusion centrality" (DC) in which semantic aspects of
the graph, as well as a diffusion model of how a diffusive property "p" is
spreading is used to characterize the centrality of vertices. DC is polynomially
computable - we present a hyper-graph based algorithm to compute DC and report
on a prototype implementation and experiments showing how we can compute DCs
(using real Youtube data) on semantic social networks of up to 100k vertices in
a reasonable amount of time. Ongoing work focuses on scaling this up.

Joint work with Chanhyn Kang, Sarit Kraus, Cristian Molinaro, and
Yuval Shavitt.

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