A testing based extraction algorithm for identifying significant communities in networks

DOI10.1214/14-AOAS760zbMath1304.05141arXiv1308.0777WikidataQ103589709 ScholiaQ103589709MaRDI QIDQ484064

James D. Wilson, Shankar Bhamidi, Peter J. Mucha, Simi Wang, Andrew B. Nobel

Publication date: 17 December 2014

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1308.0777

zbMATH Keywords

networksmultiple testingcommunity detectionbackgroundextraction

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Social networks; opinion dynamics (91D30) Graph algorithms (graph-theoretic aspects) (05C85) Paired and multiple comparisons; multiple testing (62J15)

Related Items

A testing based approach to the discovery of differentially correlated variable sets, Validation of community robustness, An Escape Time Formulation for Subgraph Detection and Partitioning of Directed Graphs, A testing based extraction algorithm for identifying significant communities in networks, Computing exact \(p\)-values for community detection, Fusing data depth with complex networks: community detection with prior information, Unnamed Item, Unnamed Item, Compressive Sensing for Cut Improvement and Local Clustering

Uses Software

• Matlab
• R
• AdaBoost.MH
• ESSC
• ElemStatLearn
• GenLouvain
• LAS

Cites Work

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