Doubly threshold graphs for social network modeling

Vida Ravanmehr, Sadegh Bolouki, Gregory J. Puleo, Olgica Milenkovic

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

Abstract

Threshold graphs are recursive deterministic network models that capture properties of certain social and economic interactions. One drawback of these graph families is that they they have limited constrained generative attachment rules. To mitigate this problem, we introduce a new class of graphs termed Doubly Threshold (DT) graphs which may be succinctly described through vertex weights that govern the existence of edges via two inequalities. One inequality imposes the constraint that the sum of weights of adjacent vertices has to exceed a specified threshold. The second inequality ensures that adjacent vertices have a bounded difference of their weights. We provide a succinct characterization and decomposition of DT graphs and analyze their forbidden induced subgraphs which we compare to those of known social networks. We also present a method for performing vertex weight assignments on DT graphs that satisfy the defining constraints.

Original languageEnglish (US)
Title of host publication2016 IEEE Information Theory Workshop, ITW 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages231-235
Number of pages5
ISBN (Electronic)9781509010905
DOIs
StatePublished - Oct 21 2016
Event2016 IEEE Information Theory Workshop, ITW 2016 - Cambridge, United Kingdom
Duration: Sep 11 2016Sep 14 2016

Publication series

Name2016 IEEE Information Theory Workshop, ITW 2016

Other

Other2016 IEEE Information Theory Workshop, ITW 2016
Country/TerritoryUnited Kingdom
CityCambridge
Period9/11/169/14/16

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Software
  • Signal Processing

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