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Nonlinear Dynamics of Information Diffusion in Social Networks

Published: 24 April 2017 Publication History

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 SpikeM, 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 SpikeM 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 SpikeStream, which identifies multiple diffusion patterns in a large collection of online event streams. Extensive experiments on real datasets demonstrate that SpikeM accurately and succinctly describes all patterns of the rise and fall spikes in social networks.

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    cover image ACM Transactions on the Web
    ACM Transactions on the Web  Volume 11, Issue 2
    May 2017
    199 pages
    ISSN:1559-1131
    EISSN:1559-114X
    DOI:10.1145/3079924
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    Publication History

    Published: 24 April 2017
    Accepted: 01 December 2016
    Revised: 01 June 2016
    Received: 01 April 2015
    Published in TWEB Volume 11, Issue 2

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    1. Information diffusion
    2. nonlinear modeling
    3. social networks

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