# Predicting Stability of Community Members in Complex Networks

## TL;DR

This paper introduces profile closeness as a new metric to predict community member stability and community evolution in both static and dynamic complex networks.

## Contribution

It demonstrates the effectiveness of profile closeness in predicting community stability and evolution, offering a novel approach for analyzing complex networks.

## Key findings

- Profile closeness correlates with community stability.
- Profile closeness predicts community evolution in static and dynamic networks.
- The method is effective across different types of complex networks.

## Abstract

In this work, we analyse and predict the stability of communities in complex networks. We use a variant of closeness centrality, known as profile closeness, to measure the loyalty of a member towards its community. We show that the profile closeness is an adequate indicator of how communities evolve in a network. We investigate this in static as well as dynamic (temporal) networks and establish the relevance of profile closeness in predicting the evolution of a complex network. Keywords: Small world networks , Centrality , Community , Closeness , Clustering