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Accounting for edge uncertainty in stochastic actor-oriented models for dynamic network analysis

Network Science(2025)10.1017/nws.2025.6Source: DataRank Database

Accounting for edge uncertainty in stochastic actor-oriented models for dynamic network analysis is a research paper published in Network Science (2025). On theSindex it has a DataRank of 0. It has been cited 2 times.

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0DataRank · unranked

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Open Access2 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (3 grants)Impact (FWCI 1.80)OA: hybridTopics (21)IDs (PubMed, PMC, OpenAlex)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (4)

Mark Kramer ORCID,Catherine J. Chu ORCID,Eric D. Kolaczyk ORCID,Heather M. Shappell ORCID

Live enrichment

PubMed:41788468 PMC:PMC12959939 OpenAlex:https://openalex.org/W4411500587

NINDS NIH HHS

Grant: R01 NS095369

NIBIB NIH HHS

Grant: K25 EB032903

NIGMS NIH HHS

Grant: T32 GM074905

FWCI

1.80

Citation Percentile

0.8%

Citation Trend

2026

hybridLicense: cc-by

journalhttps://www.cambridge.org/core/services/aop-cambridge-core/content/view/60CBC323B23AF73B46CBA40601AC06BE/S2050124225000062a.pdf/div-class-title-accounting-for-edge-uncertainty-in-stochastic-actor-oriented-models-for-dynamic-network-analysis-div.pdf

publisherhttps://www.cambridge.org/core/services/aop-cambridge-core/content/view/60CBC323B23AF73B46CBA40601AC06BE/S2050124225000062a.pdf/div-class-title-accounting-for-edge-uncertainty-in-stochastic-actor-oriented-models-for-dynamic-network-analysis-div.pdf

publisherhttps://www.cambridge.org/core/services/aop-cambridge-core/content/view/S2050124225000062

journalhttps://doi.org/10.1017/nws.2025.6

repositoryhttps://pubmed.ncbi.nlm.nih.gov/41788468

Fields of Study

Functional Brain Connectivity StudiesOpinion Dynamics and Social InfluenceComplex Network Analysis Techniques

Keywords

Computer scienceMarkov chainHidden Markov modelExpectation–maximization algorithmAlgorithmEnhanced Data Rates for GSM EvolutionArtificial intelligenceTheoretical computer scienceMachine learningMathematicsMaximum likelihoodStatisticsHidden Markov modelsSocial NetworksParticle FilteringBrain NetworksExpectation-maximization AlgorithmLongitudinal Network Analysis

Sustainable Development Goals

SDG 0: Reduced inequalities