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- Publisher Website: 10.1126/sciadv.abf1211
- Scopus: eid_2-s2.0-85105071660
- PMID: 33893102
- WOS: WOS:000656713700011
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Article: Belief propagation for networks with loops
Title | Belief propagation for networks with loops |
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Authors | |
Issue Date | 2021 |
Citation | Science Advances, 2021, v. 7, n. 17, article no. eabf1211 How to Cite? |
Abstract | 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. |
Persistent Identifier | http://hdl.handle.net/10722/317042 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Kirkley, Alec | - |
dc.contributor.author | Cantwell, George T. | - |
dc.contributor.author | Newman, M. E.J. | - |
dc.date.accessioned | 2022-09-19T06:18:40Z | - |
dc.date.available | 2022-09-19T06:18:40Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Science Advances, 2021, v. 7, n. 17, article no. eabf1211 | - |
dc.identifier.uri | http://hdl.handle.net/10722/317042 | - |
dc.description.abstract | 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. | - |
dc.language | eng | - |
dc.relation.ispartof | Science Advances | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.title | Belief propagation for networks with loops | - |
dc.type | Article | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1126/sciadv.abf1211 | - |
dc.identifier.pmid | 33893102 | - |
dc.identifier.scopus | eid_2-s2.0-85105071660 | - |
dc.identifier.volume | 7 | - |
dc.identifier.issue | 17 | - |
dc.identifier.spage | article no. eabf1211 | - |
dc.identifier.epage | article no. eabf1211 | - |
dc.identifier.eissn | 2375-2548 | - |
dc.identifier.isi | WOS:000656713700011 | - |