Accession Number:

ADA611445

Title:

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

Descriptive Note:

Journal article preprint

Corporate Author:

CALIFORNIA UNIV LOS ANGELES DEPT OF MATHEMATICS

Report Date:

2014-08-01

Pagination or Media Count:

28.0

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.

Subject Categories:

  • Information Science
  • Sociology and Law

Distribution Statement:

APPROVED FOR PUBLIC RELEASE