Analysis of Directed Signed Networks: Triangles Inventory

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Signed networks form a particular class of complex networks that has many applications in sociology, recommender and voting systems. The contribution of this paper is twofold. First, we propose an approach aimed at determining the characteristic subgraphs of the network. Second, we apply the developed approach to the analysis of the network describing the Wikipedia adminship elections. It is shown that this network agrees with the status theory if one does not consider strongly tied vertices, i.e., the vertices that are connected in both directions. At the same time, the strongly connected vertices mostly agree with the structural balance theory. This result indicates that there is a substantial difference between single and double connections, the fact that deserves a detailed analysis within a broader context of directed signed networks.

Original languageEnglish
Title of host publicationNetworks in the Global World V - Proceedings of NetGloW 2020
EditorsArtem Antonyuk, Nikita Basov
PublisherSpringer Nature
Pages120-132
Number of pages13
ISBN (Print)9783030648763
DOIs
StatePublished - 2021
Event5th Networks in the Global World Conference, NetGloW 2020 - St. Petersburg, Russian Federation
Duration: 7 Jul 20209 Jul 2020

Publication series

NameLecture Notes in Networks and Systems
Volume181
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference5th Networks in the Global World Conference, NetGloW 2020
CountryRussian Federation
CitySt. Petersburg
Period7/07/209/07/20

Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications

Keywords

  • Probabilistic methods
  • Signed graphs
  • Status theory
  • Structural balance
  • Weighted graphs

Fingerprint

Dive into the research topics of 'Analysis of Directed Signed Networks: Triangles Inventory'. Together they form a unique fingerprint.

Cite this