Title
On Mixing in Pairwise Markov Random Fields with Application to Social Networks.
Abstract
We consider pairwise Markov random fields which have a number of important applications in statistical physics, image processing and machine learning such as Ising model and labeling problem to name a couple. Our own motivation comes from the need to produce synthetic models for social networks with attributes. First, we give conditions for rapid mixing of the associated Glauber dynamics and consider interesting particular cases. Then, for pairwise Markov random fields with submodular energy functions we construct monotone perfect simulation.
Year
DOI
Venue
2016
10.1007/978-3-319-49787-7_11
ALGORITHMS AND MODELS FOR THE WEB GRAPH, WAW 2016
DocType
Volume
ISSN
Journal
10088
0302-9743
Citations 
PageRank 
References 
0
0.34
8
Authors
3
Name
Order
Citations
PageRank
Konstantin Avrachenkov11250126.17
Lenar Iskhakov200.34
Maksim Mironov301.01