  This paper proposes a network model of opinion dynamics based on both the
social network structure and network centralities. The conceptual novelty in
this model is that the opinion of each individual is weighted by the associated
network centrality in characterizing the opinion spread on social networks.
Following a degree-centrality-weighted opinion dynamics model, we provide an
algorithm to partition nodes of any graph into two and multiple clusters based
on opinion disagreements. Furthermore, the partition algorithm is applied to
real-world social networks including the Zachary karate club network [1] and
the southern woman network [2] and these application examples indirectly verify
the effectiveness of the degree-centrality-weighted opinion dynamics model.
Finally, properties of general centrality-weighted opinion dynamics model are
established.
