  Node centrality is one of the integral measures in network analysis with wide
range of applications from socio-economic to personalized recommendation. We
argue that an effective centrality measure should undertake stability even
under information loss or noise introduced in the network. With six local
information based centrality metric, we investigate the effect of varying
assortativity while keeping degree distribution unchanged, using networks with
scale free and exponential degree distribution. This model provides a novel
scope to analyze stability of centrality metric which can further finds many
applications in social science, biology, information science, community
detection and so on.
