liu.seSearch for publications in DiVA
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Direct blockmodeling of valued and binary networks: a dichotomization-free approach
Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, Institutet för analytisk sociologi, IAS. Linköpings universitet, Filosofiska fakulteten.ORCID-id: 0000-0001-5057-1985
2020 (engelsk)Inngår i: Social Networks, ISSN 0378-8733, E-ISSN 1879-2111, Vol. 61, s. 128-143Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

A long-standing open problem with direct blockmodeling is that it is explicitly intended for binary, not valued, networks. The underlying dilemma is how empirical valued blocks can be compared with ideal binary blocks, an intrinsic problem in the direct approach where partitions are solely determined through such comparisons. Addressing this dilemma, valued networks have either been dichotomized into binary versions, or novel types of ideal valued blocks have been introduced. Both these workarounds are problematic in terms of interpretability, unwanted data reduction, and the often arbitrary setting of model parameters. This paper proposes a direct blockmodeling approach that effectively bypasses the dilemma with blockmodeling of valued networks. By introducing an adaptive weighted correlation-based criteria function, the proposed approach is directly applicable to both binary and valued networks, without any form of dichotomization or transformation of the valued (or binary) data at any point in the analysis, while still using the conventional set of ideal binary blocks from structural, regular and generalized blockmodeling. The proposed approach seemingly solves two other open problems with direct blockmodeling. First, its standardized goodness-of-fit measure allows for direct comparisons across solutions, within and between networks of different sizes, value types, and notions of equivalence. Secondly, through an inherent bias of point-biserial correlations, the approach puts a premium on solutions that are closer to the mid-point density of blockmodels. This, it is argued, translates into solutions that are more intuitive and easier to interpret. The approach is demonstrated by structural, regular and generalized blockmodeling applications of six classical binary and valued networks. Finding feasible and intuitive optimal solutions in both the binary and valued examples, the approach is proposed not only as a practical, dichotomization-free heuristic for blockmodeling of valued networks but also, through its additional benefits, as an alternative to the conventional direct approach to blockmodeling.

sted, utgiver, år, opplag, sider
ELSEVIER , 2020. Vol. 61, s. 128-143
Emneord [en]
Blockmodeling; Valued networks; Goodness-of-fit functions; Weighted correlation coefficient; Dichotomization
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-164028DOI: 10.1016/j.socnet.2019.10.004ISI: 000512481500011OAI: oai:DiVA.org:liu-164028DiVA, id: diva2:1411789
Tilgjengelig fra: 2020-03-04 Laget: 2020-03-04 Sist oppdatert: 2020-05-19

Open Access i DiVA

Fulltekst tilgjengelig fra 2021-12-25 06:00
Tilgjengelig fra 2021-12-25 06:00

Andre lenker

Forlagets fulltekst

Søk i DiVA

Av forfatter/redaktør
Nordlund, Carl
Av organisasjonen
I samme tidsskrift
Social Networks

Søk utenfor DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 2 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf