On the equivalence of the edge/isolate and edge/concurrent tie ERGM families, and their extensions
Bibliographic Data
| ID | 10142306 |
|---|---|
| Authors | C T Butts (0000-0002-7911-9834, University of California, Irvine, corresponding author) |
| Year | 2016 |
| Volume | 40 |
| Issue | 1 |
| Pages | 1-6 |
| Publication date | 2016-01-02 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Mathematical Sociology (JOURNAL) |
| Journal identifiers | ISSN: 0022-250X • E-ISSN: 1545-5874 |
| Publisher | Taylor & Francis (PUBLISHER • GB) |
| DOI | 10.1080/0022250x.2015.1112386 |
| PMID | 27594715 |
| OpenAlex | W2223919427 |
| Language | EN |
| Citations received | 2 |
| References cited | 16 |
Partnership concurrency is a major driver of permeability of social networks to diffusion, and an important modeling target in the context of sexually transmitted infections. A seemingly unrelated phenomenon of concern in modeling social networks is isolation avoidance-the tendency of individuals to maintain at least one tie. Although concurrency bias and bias in isolate formation would naively seem to be distinct, we here show that their respective ERGM expressions (edge/concurrent tie and edge/isolate families, and their regular extensions) are equivalent, and that both are equivalent to a special case of the geometrically weighted degree families. In addition to being statistically useful, this equivalence provides insight into the essential connection between these apparently different structural phenomena
Discrete mathematics · Enhanced Data Rates for GSM Evolution · Equivalence (formal languages · Advanced Topics in Algebra · Artificial Intelligence · Computer Science · Mathematics · Matrix Theory and Algorithms · PAPR reduction in OFDM · Psychology
Ergm
Markov Graphs
Logit Models and Logistic Regressions for Social Networks
Inference in Curved Exponential Family Models for Networks
Advances in exponential random graph (p*) models
Advances in exponential random graph (p*) models applied to a large social network
Curved exponential family models for social networks
Concurrent partnerships and transmission dynamics in networks
Conditional Marginalization for Exponential Random Graph Models
A Novel Simulation Method for Binary Discrete Exponential Families, With Application to Social Networks
Bernoulli Graph Bounds for Generalrandom Graphs
New Specifications for Exponential Random Graph Models
| Citations per year | 0.33 |
|---|---|
| Citation span | 2020 - 2023 (4) |
| Highly cited | No |