Hierarchical Network Models for Exchangeable Structured Interaction Processes

Network data often arises via a series of structured interactions among a population of constituent elements. E-mail exchanges, for example, have a single sender followed by potentially multiple receivers. Scientific articles, on the other hand, may have multiple subject areas and multiple authors....

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Veröffentlicht in:Journal of the American Statistical Association Jg. 117; H. 540; S. 2056 - 2073
Hauptverfasser: Dempsey, Walter, Oselio, Brandon, Hero, Alfred
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Taylor & Francis 02.10.2022
Taylor & Francis Ltd
Schlagworte:
Algorithms
Data
data collection
Datasets
e-mail
Electronic mail
Email
Exchangeability
Goodness of fit
Hierarchies
Interaction-labeled networks
Internet
Law
Morality
Network models
Networks
Population distribution
Power
Power law
Property law
Regression analysis
Sampling
Scientific papers
Social networks
Sparsity
Statistical methods
Statistical models
Statistics
Structured interaction data
subfamily
exchangeability
network models
sparsity
structured interaction data
interaction-labeled networks
power law
ISSN:0162-1459, 1537-274X, 1537-274X
Online-Zugang:Volltext
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Zusammenfassung:Network data often arises via a series of structured interactions among a population of constituent elements. E-mail exchanges, for example, have a single sender followed by potentially multiple receivers. Scientific articles, on the other hand, may have multiple subject areas and multiple authors. We introduce a statistical model, termed the Pitman-Yor hierarchical vertex components model (PY-HVCM), that is well suited for structured interaction data. The proposed PY-HVCM effectively models complex relational data by partial pooling of local information via a latent, shared population-level distribution. The PY-HCVM is a canonical example of hierarchical vertex components models-a subfamily of models for exchangeable structured interaction-labeled networks, that is, networks invariant to interaction relabeling. Theoretical analysis and supporting simulations provide clear model interpretation, and establish global sparsity and power law degree distribution. A computationally tractable Gibbs sampling algorithm is derived for inferring sparsity and power law properties of complex networks. We demonstrate the model on both the Enron e-mail dataset and an ArXiv dataset, showing goodness of fit of the model via posterior predictive validation.
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ISSN:0162-1459
1537-274X
1537-274X
DOI:10.1080/01621459.2021.1896526