The DOI matches its Crossref record, with a small difference in title or year; 297 of the 297 references it cites were found in a registry or an index.
Social Networks and Health: Models, Methods, and Applications
Abstract
Social Networks and Health provides a comprehensive introduction to how social networks influence health behaviors. Section one provides an introduction to major research themes and perspectives used to understand how networks form, evolve, and channel the spread of ideas and behaviors. An intellectual history of the field is provided as well as conjectures on why network science took so long to develop. Methodologies for studying networks and assessing personal network data are discussed. Section two covers algorithms and applications of the most common network metrics divided into four chapters: centrality, groups, positions, and network level. For each chapter, descriptions of how the metrics are calculated and how they influence health behavior are presented. Section three reviews applications of social network analysis to health behaviors. The actor-oriented stochastic evolution model is presented first which provides a way to statistically test network evolution properties. Diffusion of innovations models are presented next which describe how networks influence the spread of ideas and practices within and between communities. Network interventions are also presented and a typology describing network interventions and evidence from empirical studies presented. This book enables researchers to understand how network data are collected and processed; and how to calculate appropriate metrics and models used to understand network influences on health behavior. Simple examples and data are presented throughout so researchers can adopt this methodology and perspective in their own investigations. Examples of health behaviors include smoking, substance use, contraception, HIV/AIDS, obesity, and many others.
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“For example, interorganizational networks may contribute to the adoption and utilization of practices that may be with or without an evidence base (Greenhalgh et al., 2004; Valente, 2010; Valente et al., 2015).”
From Implementation-as-Usual in Community-Based Organizations Providing Specialized Services to Individuals with Autism Spectrum Disorder: A Mixed Methods Study (2021) · cites this paper as Valente, 2010
In the citing paper
“Yet interventions implemented by community-identified leaders are often more effective than those by non-leaders [20, 21].”
From Social Network Analysis for Program Implementation (2015) · cites this paper as [21]
In the citing paper
“Individuals change their behaviors because they perceive themselves to have the personal supports to change, because they see the new behavior as normative, and because they see how it benefits themselves and those around them [21].”
From Social Network Analysis for Program Implementation (2015) · cites this paper as [21]
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Valente, T. W. (2012) · Science · doi:10.1126/science.1217330 · Cited by 1,271
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Social Network Analysis for Program Implementation
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Metadata
- Authors
- Valente, T. W.
- Affiliations
- University of Southern California (US)
- Corresponding author
- Valente, Thomas W.
- ORCID
- Valente, Thomas W.
- Publisher
- Oxford University PressNew York
- Published
- 2010-02-25
- OpenAlex
- W2492628744
- Cited by
- 933
- References
- 297
- Citations by year
- 2026: 69 · 2025: 74 · 2024: 52 · 2023: 58 · 2022: 65 · 2021: 74 · 2020: 61 · 2019: 64 · 2018: 82 · 2017: 62 · 2016: 68 · 2015: 52 · 2014: 54 · 2013: 37 · 2012: 33
- FWCI
- 7.65
- Type
- book
- Language
- en
- Open access
- closed
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Health › Health disparities and outcomes
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Community Health and Development
- Keywords
- social networks, health behavior, social network analysis, network metrics, centrality, network position, network evolution, diffusion of innovations, network intervention, smoking, substance use, contraception, HIV/AIDS, obesity
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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 ›
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