Inferring directed static networks of influence from undirected temporal networks

Taro Takaguchi, Nobuo Sato, Kazuo Yano, Naoki Masuda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

A temporal network consists of a time series of interaction events, each of which is defined by a triplet composed of the indices of two nodes and the time of the event. Mapping a temporal network to a more tractable static network is often useful. A mapping method was recently proposed on the basis of the so-called transfer entropy (G. V. Steeg and A. Galstyan, in Proc. the 21st Int. Conf. WWW, p.509, 2012). In the proposed method, one generates the directed network of influence in which a directed link represents the causal relationship between activity patterns at two nodes. However, the significance of the inferred links and the sensitivity of results to the parameter values are still unclear. We propose a bootstrap sampling method to statistically configure the directed network of influence. We apply our method to the face-to-face interaction logs between office workers in Japanese companies.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE 37th Annual Computer Software and Applications Conference, COMPSAC 2013
PublisherIEEE Computer Society
Pages155-156
Number of pages2
ISBN (Print)9780769549866
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event2013 IEEE 37th Annual Computer Software and Applications Conference, COMPSAC 2013 - Kyoto, Japan
Duration: 2013 Jul 222013 Jul 26

Publication series

NameProceedings - International Computer Software and Applications Conference
ISSN (Print)0730-3157

Conference

Conference2013 IEEE 37th Annual Computer Software and Applications Conference, COMPSAC 2013
Country/TerritoryJapan
CityKyoto
Period13/7/2213/7/26

Keywords

  • Complex networks
  • Data analysis
  • Temporal networks
  • Transfer entropy

ASJC Scopus subject areas

  • Software
  • Computer Science Applications

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