Nonlinear Dynamics of Information Diffusion in Social Networks
Abstract
The recent explosion in the adoption of search engines and new media such as blogs and Twitter have facilitated the faster propagation of news and rumors. How quickly does a piece of news spread over these media? How does its popularity diminish over time? Does the rising and falling pattern follow a simple universal law? In this article, we propose S pike M, a concise yet flexible analytical model of the rise and fall patterns of information diffusion. Our model has the following advantages. First, unification power: it explains earlier empirical observations and generalizes theoretical models including the SI and SIR models. We provide the threshold of the take-off versus die-out conditions for S pike M and discuss the generality of our model by applying it to an arbitrary graph topology. Second, practicality: it matches the observed behavior of diverse sets of real data. Third, parsimony: it requires only a handful of parameters. Fourth, usefulness: it makes it possible to perform analytic tasks such as forecasting, spotting anomalies, and interpretation by reverse engineering the system parameters of interest (quality of news, number of interested bloggers, etc.). We also introduce an efficient and effective algorithm for the real-time monitoring of information diffusion, namely S pike S tream , which identifies multiple diffusion patterns in a large collection of online event streams. Extensive experiments on real datasets demonstrate that S pike M accurately and succinctly describes all patterns of the rise and fall spikes in social networks.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Apr 24, 2017
- Source ID
- 10.1145/3057741
Entities
People
- B. Aditya Prakash
- Christos Faloutsos
- Lei Li
- Yasuko Matsubara
- Yasushi Sakurai
Organizations
- Carnegie Mellon University
- Kumamoto University
- National Endowment for the Humanities
- National Science Foundation
- Oak Ridge National Laboratory
- United States Army Research Laboratory
- Virginia Tech