From which world is your graph? release_hzqxh7ldmbfklnvywt57mxqwuq

by Cheng Li, Felix Wong, Zhenming Liu, Varun Kanade

Released as a article .

2017  

Abstract

Discovering statistical structure from links is a fundamental problem in the analysis of social networks. Choosing a misspecified model, or equivalently, an incorrect inference algorithm will result in an invalid analysis or even falsely uncover patterns that are in fact artifacts of the model. This work focuses on unifying two of the most widely used link-formation models: the stochastic blockmodel (SBM) and the small world (or latent space) model (SWM). Integrating techniques from kernel learning, spectral graph theory, and nonlinear dimensionality reduction, we develop the first statistically sound polynomial-time algorithm to discover latent patterns in sparse graphs for both models. When the network comes from an SBM, the algorithm outputs a block structure. When it is from an SWM, the algorithm outputs estimates of each node's latent position.
In text/plain format

Archived Files and Locations

application/pdf  1.4 MB
file_ivzcymq3hzcstaicf4ehsu2gve
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2017-11-03
Version   v1
Language   en ?
arXiv  1711.00982v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 57bbc747-cb5d-4e79-8b4b-1e58eefeb8d1
API URL: JSON