The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
On a General Class of Models for Interaction
Abstract
This paper develops a class of probability models for configurations of interacting points in a domain. The distributions depend on a function which may be viewed as giving the potential energy of the configurations. Examples include models for interaction in a spatial region and on lattices and graphs. New models in the general class arise naturally, an example being a spatial model for points of different categories. Some general methods, including series expansions and a simulation method known from statistical mechanics, can be useful for many of these models. Several applications of this kind are considered, and some connections between the models and with the statistical mechanics are explored.
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
Cites
Ripley, B. D. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01615.x · Cited by 2,511
Cited by
The Structure and Function of Complex Networks
Newman, M. E. J. (2003) · SIAM Review · doi:10.1137/s003614450342480 · Cited by 18,719
Critical phenomena in complex networks
Dorogovtsev, S. N., Goltsev, A. V., & Mendes, J. F. F. (2008) · Reviews of Modern Physics · doi:10.1103/revmodphys.80.1275 · Cited by 2,220
Logit Models and Logistic Regressions for Social Networks: I. An Introduction to Markov Graphs and p
Wasserman, S., & Pattison, P. (1996) · Psychometrika · doi:10.1007/bf02294547 · Cited by 950
Logit models and logistic regressions for social networks: II. Multivariate relations
Pattison, P., & Wasserman, S. (1999) · British Journal of Mathematical and Statistical Psychology · doi:10.1348/000711099159053 · Cited by 327
Configuring Random Graph Models with Fixed Degree Sequences
Fosdick, B. K., Larremore, D. B., Nishimura, J., & Ugander, J. (2018) · SIAM Review · doi:10.1137/16m1087175 · Cited by 246
Metadata
- Authors
- Strauss, D.
- Venue
- SIAM Review
- Published
- 1986-12
- Volume
- 28
- Issue
- 4
- Pages
- 513-527
- ISSN
- 0036-1445 (ISSN-L)
- 1095-7200
- OpenAlex
- W2078578908
- Cited by
- 247
- References
- 1
- FWCI
- 1.30
- Type
- journal article
- 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: On a General Class of Models for Interaction. https://app.cochat.ai/papers/10.1137/1028156
- The paper
- Strauss, D. (1986). On a General Class of Models for Interaction. SIAM Review. https://doi.org/10.1137/1028156
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.
