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Cited by 295

The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.

Exponential-family random graph models for valued networks

Open access
Krivitsky, Pavel N.·2012·Electronic Journal of Statistics
DOI 10.1214/12-ejs696
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Abstract

Exponential-family random graph models (ERGMs) provide a principled and flexible way to model and simulate features common in social networks, such as propensities for homophily, mutuality, and friend-of-a-friend triad closure, through choice of model terms (sufficient statistics). However, those ERGMs modeling the more complex features have, to date, been limited to binary data: presence or absence of ties. Thus, analysis of valued networks, such as those where counts, measurements, or ranks are observed, has necessitated dichotomizing them, losing information and introducing biases. In this work, we generalize ERGMs to valued networks. Focusing on modeling counts, we formulate an ERGM for networks whose ties are counts and discuss issues that arise when moving beyond the binary case. We introduce model terms that generalize and model common social network features for such data and apply these methods to a network dataset whose values are counts of interactions.

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Metadata

Authors
Krivitsky, P. N.
Venue
Electronic Journal of Statistics
Publisher
Institute of Mathematical Statistics
Published
2012-01-01
Volume
6
Issue
none
Pages
1100-1128
ISSN
1935-7524 (ISSN-L)
DOI
10.1214/12-ejs696
OpenAlex
W2024818197
Cited by
295
References
47
FWCI
10.46
Type
journal article
Language
en
Open access
gold

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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