The DOI matches its Crossref record.
Generalized Blockmodeling
Abstract
This book provides an integrated treatment of blockmodeling, the most frequently used technique in social network analysis. It secures its mathematical foundations and then generalizes blockmodeling for the analysis of many types of network structures. Examples are used throughout the text and include small group structures, little league baseball teams, intra-organizational networks, inter-organizational networks, baboon grooming networks, marriage ties of noble families, trust networks, signed networks, Supreme Court decisions, journal citation networks, and alliance networks. Also provided is an integrated treatment of algebraic and graph theoretic concepts for network analysis and a broad introduction to cluster analysis. These formal ideas are the foundations for the authors' proposal for direct optimizational approaches to blockmodeling which yield blockmodels that best fit the data, a measure of fit that is integral to the establishment of blockmodels, and creates the potential for many generalizations and a deductive use of blockmodeling.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014) · Journal of Complex Networks · doi:10.1093/comnet/cnu016 · Cited by 3,235
Valente, T. W. (2012) · Science · doi:10.1126/science.1217330 · Cited by 1,271
Clustering in weighted networks
Opsahl, T., & Panzarasa, P. (2009) · Social Networks · doi:10.1016/j.socnet.2009.02.002 · Cited by 1,037
Social Networks and Health: Models, Methods, and Applications
Valente, T. W. (2010) · doi:10.1093/acprof:oso/9780195301014.001.0001 · Cited by 933
Metadata
- Authors
- Doreian, P., Batagelj, V., & Ferligoj, A.
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2004-11-08
- OpenAlex
- W977705565
- Cited by
- 472
- References
- 0
- FWCI
- 2.09
- Type
- book
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Generalized Blockmodeling. https://app.cochat.ai/papers/10.1017/cbo9780511584176
- The paper
- Doreian, P., Batagelj, V., & Ferligoj, A. (2004). Generalized Blockmodeling. Cambridge University Press eBooks. https://doi.org/10.1017/cbo9780511584176
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
