Use of transfer entropy to infer relationships from behavior

Authors:
Travis L. Bauer;Rich Colbaugh;Kristin Glass;David Schnizlein
Affiliations:
Sandia National Laboratories, Albuquerque, NM;Sandia National Laboratories, Albuquerque, NM;New Mexico Tech, Socorro, NM;Sandia National Laboratories, Shoreview, MN
Venue:
Proceedings of the Eighth Annual Cyber Security and Information Intelligence Research Workshop
Year:
2013

Citing 4
Cited 0

Inferring Gene Regulatory Networks from Microarray Time Series Data Using Transfer Entropy

CBMS '07 Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems
Feedback effects between similarity and social influence in online communities

Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding social roles in Wikipedia

Proceedings of the 2011 iConference
Social networks of Wikipedia

Proceedings of the 22nd ACM conference on Hypertext and hypermedia

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper discusses the use of transfer entropy to infer relationships among entities. This is useful when one wants to understand relationships among entities but can only observe their behavior, but not direct interactions with one another. This is the kind of environment prevelant in network monitoring, where one can observe behavior coming into and leaving a network from many different hosts, but cannot directly observe which hosts are related to one another. In this paper, we show that networks of individuals inferred using the transfer entropy of Wikipedia editing behavior predicts observed "ground truth" social networks. At low levels of recall, transfer entropy can extract these social networks with a precision approximately 20 times higher than would be expected by chance. We'll discuss the algorithm, the data set, and various parameter considerations when attempting to apply this algorithm to a data set.