Point-Process Models of Social Network Interactions: Parameter Estimation and Missing Data Recovery

Abstract

Electronic communications, as well as other categories of interactions within social networks, exhibit bursts of activity localised in time. We adopt a self-exciting Hawkes process model for this behaviour. First we investigate parameter estimation of such processes and find that the choice of triggering function is not as important as getting the correct parameters once a choice is made. Then we present a relaxed maximum likelihood method for filling in missing data in records of communications in social networks. Finally we demonstrate the method using a data set composed of email records from a social network based at the United States Military Academy. The method performs differently on this data and data from simulations, but the performance degrades only slightly as more information is removed. The ability to fill in large blocks of missing social network data has implications for security, surveillance, and privacy.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 2014
Accession Number
ADA611445

Entities

People

  • Andrea Bertozzi
  • Frederic P. Schoenberg
  • Joseph R. Zipkin
  • Kathryn Coronges

Organizations

  • University of California, Los Angeles

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Applied Mathematics
  • Calorific Value
  • Data Sets
  • Electronic Mail
  • Equations
  • Geometry
  • Mathematics
  • Mobile Phones
  • National Security
  • Probability
  • Random Variables
  • Social Media
  • Social Networks
  • Statistics
  • United States
  • United States Military Academy

Readers

  • Applied Combinatorial Optimization and Logic Circuit Design.
  • Computer Networking
  • Theoretical Analysis.

Technology Areas

  • Microelectronics