Community evolution in a scientific collaboration network

Abstract

A community in a network is a set of nodes with a larger density of intra-community links than inter-community links. Tracking communities in a network via a community life-cycle model can reveal patterns on how the network evolve. Previous models of community life-cycle provided a first step towards analyzing how communities change over time. We introduce an extended life-cycle model having the minimum community size as a parameter. Our model is capable of uncovering anomaly in community evolution and dynamics such as communities with stable or stagnant size. We apply our model to track, and uncover trends in, the evolution of communities of genetic programming researchers. The lifespan of a community measures how long it has lived. The distribution of lifespan in the network of genetic programming researchers is shown to be modeled as an exponential-law, a phenomenon yet to be explored in other empirical networks. We show that our parameter of minimum community size can significantly affect how communities grow over time. The parameter is fine-tuned to detect anomaly in community evolution.