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Calling patterns in human communication dynamics.

Zhi-Qiang Jiang1, Wen-Jie Xie, Ming-Xia Li

  • 1School of Business, East China University of Science and Technology, Shanghai 200237, China. zqjiang@ecust.edu.cn

Proceedings of the National Academy of Sciences of the United States of America
|January 16, 2013
PubMed
Summary
This summary is machine-generated.

Most cell phone users exhibit Weibull distribution in communication patterns, unlike a small group showing power-law distributions linked to fraud or sales. This research distinguishes ordinary users from anomalous callers based on intercall durations.

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Area of Science:

  • Network Science
  • Statistical Physics
  • Communication Engineering

Background:

  • Modern communication technologies generate extensive electronic data, revealing temporal interaction patterns.
  • Analysis of these patterns, particularly intercall durations, offers insights into user behavior and network dynamics.

Purpose of the Study:

  • To investigate the statistical distribution of intercall durations among a large cohort of mobile phone users.
  • To differentiate user communication patterns and identify distinct user clusters based on these distributions.

Main Methods:

  • Statistical analysis of intercall durations for 100,000 active mobile phone users.
  • Application of statistical tests to determine power-law and Weibull distribution adherence at individual user levels.
  • Quantification of individual user behavior using metrics like out-degree and communication diversity.

Main Results:

  • At the population level, intercall durations follow a power-law distribution with an exponential cutoff.
  • Individually, only 3.46% of users exhibit power-law distributions, often associated with robot-based calling, fraud, or sales.
  • The majority (73.34%) of users display Weibull distributions, characterizing ordinary communication patterns.

Conclusions:

  • Individual communication patterns deviate significantly from population-level distributions.
  • Distinguishing between Weibull (ordinary users) and power-law (anomalous users) distributions is crucial for understanding user behavior and network anomalies.
  • Findings provide a framework for analyzing large-scale communication data and identifying specific user groups for mobile operators.