Journal article
Node Features Adjusted Stochastic Block Model
Journal of computational and graphical statistics, Vol.28(2), pp.362-373
04/03/2019
DOI: 10.1080/10618600.2018.1530117
Abstract
Stochastic block model (SBM) and its variants are popular models used in community detection for network data. In this article, we propose a feature-adjusted stochastic block model (FASBM) to capture the impact of node features on the network links as well as to detect the residual community structure beyond that explained by the node features. The proposed model can accommodate multiple node features and estimate the form of feature impacts from the data. Moreover, unlike many existing algorithms that are limited to binary-valued interactions, the proposed FASBM model and inference approaches are easily applied to relational data that generate from any exponential family distribution. We illustrate the methods on simulated networks and on two real-world networks: a brain network and an US air-transportation network.
Details
- Title: Subtitle
- Node Features Adjusted Stochastic Block Model
- Creators
- Yun Zhang - Department of Statistics, University of PittsburghKehui Chen - Department of Statistics, University of PittsburghAllan Sampson - Department of Statistics, University of PittsburghKai Hwang - Helen Wills Neuroscience Institute, University of California BerkeleyBeatriz Luna - Center for the Neural Basis of Cognition, University of Pittsburgh
- Resource Type
- Journal article
- Publication Details
- Journal of computational and graphical statistics, Vol.28(2), pp.362-373
- Publisher
- Taylor & Francis
- DOI
- 10.1080/10618600.2018.1530117
- ISSN
- 1061-8600
- eISSN
- 1537-2715
- Grant note
- NSF 1612458 / Funding Kehui Chen's
- Language
- English
- Date published
- 04/03/2019
- Academic Unit
- Psychiatry; Psychological and Brain Sciences; Iowa Neuroscience Institute
- Record Identifier
- 9984002306202771
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