التفاصيل البيبلوغرافية
| العنوان: |
Node Features Adjusted Stochastic Block Model. |
| المؤلفون: |
Zhang, Yun1 (AUTHOR), Chen, Kehui1 (AUTHOR) khchen@pitt.edu, Sampson, Allan1 (AUTHOR), Hwang, Kai2 (AUTHOR), Luna, Beatriz3,4 (AUTHOR) |
| المصدر: |
Journal of Computational & Graphical Statistics. Apr-Jun2019, Vol. 28 Issue 2, p362-373. 12p. |
| مصطلحات موضوعية: |
*STOCHASTIC models, COMMUNITY organization, EXPONENTIAL families (Statistics) |
| مستخلص: |
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. [ABSTRACT FROM AUTHOR] |
|
Copyright of Journal of Computational & Graphical Statistics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Business Source Index |
