Predicting Stability of Community Members in Complex Networks using profile closeness

Abstract

This work introduces profile closeness, a novel variant of closeness centrality, to analyze and predict community evolution
in complex networks. By prioritizing influential nodes within communities, profile closeness serves as a reliable indica- tor
of community dynamics in both static and dynamic networks. This metric can predict node addition and departure,
offering valuable insights into how commu- nities evolve over time. Furthermore, integrating profile closeness with
established research on epidemic spread reveals its potential in identifying key individuals for targeted interventions and
predicting outbreaks. Our findings underscore the importance of understanding the nuanced interplay between
community struc- ture and individual node characteristics for effectively modeling and mitigating the spread of diseases in
complex networks.
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