Cascading collapse of online social networks

TL;DR

This paper investigates the mechanisms behind rapid collapses of online social networks, identifying early user loss and cascading failures as key factors, and proposes a threshold model to predict collapse timing.

Contribution

It introduces a generalized threshold model to explain social network collapses and demonstrates how to estimate collapse time from user dynamics.

Findings

Loosely bound users disappear early in collapse

Cascading failures follow initial user loss

Collapse time can be predicted from user churn dynamics

Abstract

Online social networks have increasing influence on our society, they may play decisive roles in politics and can be crucial for the fate of companies. Such services compete with each other and some may even break down rapidly. Using social network datasets we show the main factors leading to such a dramatic collapse. At early stage mostly the loosely bound users disappear, later collective effects play the main role leading to cascading failures. We present a theory based on a generalised threshold model to explain the findings and show how the collapse time can be estimated in advance using the dynamics of the churning users. Our results shed light to possible mechanisms of instabilities in other competing social processes.