Quantifying structural patterns of information cascades

Chengxi Zang, Peng Cui, Chaoming Song, Christos Faloutsos, Wenwu Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations


Information cascades are ubiquitous in both physical society and online social media, taking on large variations in structures, dynamics and semantics. Although there has been much progress on understanding the dynamics and semantics of information cascades, little is known about their structural patterns. In this paper, we explore a large-scale dataset including 432 million information cascades with explicit records of spreading traces. We find that the structural complexity of information cascades is far beyond the previous conjectures. We first propose seven-dimensional metrics, which reflect size and spreading orientation aspects, to quantify the structural characteristics of millions of information cascades. Further, we analyze the correlations of these metrics, finding some brand new structure patterns of information cascades, potentially providing insights into intrinsic mechanisms governing information spreading in nature and new models to forecast as well as to impose good control over information cascades in real applications.

Original languageEnglish (US)
Title of host publication26th International World Wide Web Conference 2017, WWW 2017 Companion
PublisherInternational World Wide Web Conferences Steering Committee
Number of pages2
ISBN (Electronic)9781450349147
StatePublished - 2017
Event26th International World Wide Web Conference, WWW 2017 Companion - Perth, Australia
Duration: Apr 3 2017Apr 7 2017

Publication series

Name26th International World Wide Web Conference 2017, WWW 2017 Companion


Other26th International World Wide Web Conference, WWW 2017 Companion


  • Information cascades
  • Social networks
  • Structures

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications


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