Evidence map›Paper›PMID 25395989›Full record

ArticleJournal of physics. A, Mathematical and theoretical2013

Recruitment dynamics in adaptive social networks.

Maxim S Shkarayev, Ira B Schwartz, Leah B Shaw

Abstract read
In one paragraph

Article in Journal of physics. A, Mathematical and theoretical, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Maxim S ShkarayevApplied Science Department, College of William & Mary, Williamsburg, VA 23187.
Ira B SchwartzNonlinear Systems Dynamics Section, Plasma Physics Division, Code 6792, US N aval Research Laboratory, Washington, DC 20375.
Leah B ShawApplied Science Department, College of William & Mary, Williamsburg, VA 23187.

Funding

Multi-scale modeling of infectious diseases in fluctuating environmentsR01GM090204 · NIGMS · MONTCLAIR STATE UNIVERSITY · PI BILLINGS, LORA · 2009 to 2011
$791k
NIGMS NIH HHS R01 GM090204
6 · The paper itself

Abstract

We model recruitment in adaptive social networks in the presence of birth and death processes. Recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. Only a susceptible subset of nodes can be recruited. The recruiting individuals may adapt their connections in order to improve recruitment capabilities, thus changing the network structure adaptively. We derive a mean field theory to predict the dependence of the growth threshold of the recruiting class on the adaptation parameter. Furthermore, we investigate the effect of adaptation on the recruitment level, as well as on network topology. The theoretical predictions are compared with direct simulations of the full system. We identify two parameter regimes with qualitatively different bifurcation diagrams depending on whether nodes become susceptible frequently (multiple times in their lifetime) or rarely (much less than once per lifetime).

Indexed as

adaptive networksnetwork dynamicsrecruitment

Identifiers

PMID25395989
PMCPMC4227312

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.