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Role of persistent cascades in diffusion.

Steven Morse1, Marta C González2, Natasha Markuzon3

  • 1Operations Research Center, Massachusetts Institute of Technology and The Charles Stark Draper Laboratory, Cambridge, Massachusetts 02139, USA.

Physical Review. E
|February 21, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces persistent cascades, recurring communication patterns in large networks. Analysis reveals these cascades are more structured than random networks, highlighting hierarchies and hidden spreaders in information dissemination.

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Area of Science:

  • Network Science
  • Information Dissemination
  • Computational Social Science

Background:

  • Real-world communication networks exhibit complex patterns of information spread.
  • Understanding these patterns is crucial for modeling information diffusion and social dynamics.

Purpose of the Study:

  • To define and analyze persistent cascades, a structural property of large-scale communication networks.
  • To model the recurring structure of information dissemination and compare it to random network models.
  • To identify characteristics of information spread, such as hierarchies and hidden spreaders.

Main Methods:

  • Utilized inexact tree matching and agglomerative clustering to group communication patterns.
  • Extended epidemic modeling techniques to analytically model network structure.
  • Employed network simulations to assess the impact of cascade membership on information reception.

Main Results:

  • Identified persistent cascades as recurring communication patterns in real-world networks.
  • Found that real network cascades are significantly larger and more recurrent than predicted by random network models.
  • Discovered evidence of habitual spreading hierarchies, distinct weekday/weekend spreading roles, and hidden spreaders.
  • Demonstrated that membership in a cascade increases the likelihood of receiving information.

Conclusions:

  • Persistent cascades represent a fundamental structural property of information dissemination in large networks.
  • Real-world information spread deviates significantly from random processes, exhibiting predictable structures.
  • The findings offer insights into social influence, information diffusion dynamics, and network topology.