Community Evolution of Social Network: Feature, Algorithm and Model

TL;DR

This paper introduces a new method for tracking community evolution in social networks using core nodes, revealing dynamic features and distinguishing social from nonsocial networks.

Contribution

It proposes a novel core-based algorithm, CommTracker, for effectively tracking community evolution and introduces coefficients GROWTH and METABOLISM to characterize social network dynamics.

Findings

Discovery of two unique phenomena in social networks.

Introduction of GROWTH and METABOLISM coefficients.

Development of a social network model exhibiting dynamic features.

Abstract

Researchers have devoted themselves to exploring static features of social networks and further discovered many representative characteristics, such as power law in the degree distribution and assortative value used to differentiate social networks from nonsocial ones. However, people are not satisfied with these achievements and more and more attention has been paid on how to uncover those dynamic characteristics of social networks, especially how to track community evolution effectively. With these interests, in the paper we firstly display some basic but dynamic features of social networks. Then on its basis, we propose a novel core-based algorithm of tracking community evolution, CommTracker, which depends on core nodes to establish the evolving relationships among communities at different snapshots. With the algorithm, we discover two unique phenomena in social networks and further propose two representative coefficients: GROWTH and METABOLISM by which we are also able to distinguish social networks from nonsocial ones from the dynamic aspect. At last, we have developed a social network model which has the capabilities of exhibiting two necessary features above.