Data Driven Modeling of Continuous Time Information Diffusion in Social Networks

Liang Liu, Bin Chen, Bo Qu, Lingnan He, Xiaogang Qiu

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)

Abstract

Online social networks can detailedly and accurately record the activities of human beings and the trajectories of information dissemination over time, which provides us an opportunity to understand the information diffusion process from a renewed, more realistic, data driven modeling dimensionality. In consideration of two fundamental behaviors (viewing and sharing) involved in information diffusion, we propose a stochastic, heterogeneous, continuous-time delay Unknown-View-Share-Removed (UVSR) model to characterize the information diffusion process. The UVSR model introduces four parameters to describe the diffusion probability and speed: viewing/sharing probability/delay. These parameters are subject to some sort of distributions from the actual data, or based on empirical assumptions. To validate the model, we collect and analyze large number of information cascades (tree structure) diffused in WeChat network. We find that the viewing delay and sharing delay are approximately subject to log-normal and power-law distributions respectively, and the sharing probability follows a Gaussian distribution. Driven by these empirical findings and a constant viewing probability assumption, our model can reproduce numerous key features of information diffusion process in both topology and temporal dynamics, such as cascade size distribution, structural virality, life span distribution and relative propagation speed. Our work contributes to a better understanding of the topological features and temporal dynamics of information diffusion from a continuous time, stochastic modeling view.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 2nd International Conference on Data Science in Cyberspace, DSC 2017
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages655-660
Number of pages6
ISBN (Electronic)978-1-5386-1600-0
DOIs
Publication statusPublished - 2017
EventIEEE DSC 2017: 2nd IEEE International Conference on Data Science in Cyberspace - Shenzhen, China
Duration: 26 Jun 201729 Jun 2017
Conference number: 2
http://www.ieee-dsc.org/2017/

Conference

ConferenceIEEE DSC 2017
CountryChina
CityShenzhen
Period26/06/1729/06/17
Internet address

Keywords

  • Data driven modeling
  • Information diffusion
  • Social networks
  • WeChat

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