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Title: Belief propagation for networks with loops
Belief propagation is a widely used message passing method for the solution of probabilistic models on networks such as epidemic models, spin models, and Bayesian graphical models, but it suffers from the serious shortcoming that it works poorly in the common case of networks that contain short loops. Here, we provide a solution to this long-standing problem, deriving a belief propagation method that allows for fast calculation of probability distributions in systems with short loops, potentially with high density, as well as giving expressions for the entropy and partition function, which are notoriously difficult quantities to compute. Using the Ising model as an example, we show that our approach gives excellent results on both real and synthetic networks, improving substantially on standard message passing methods. We also discuss potential applications of our method to a variety of other problems.  more » « less
Award ID(s):
2005899 1838251
NSF-PAR ID:
10280418
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Science Advances
Volume:
7
Issue:
17
ISSN:
2375-2548
Page Range / eLocation ID:
eabf1211
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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