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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2510.19231 |
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| _version_ | 1866909863442907136 |
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| author | Kim, Seongmin Kirkley, Alec |
| author_facet | Kim, Seongmin Kirkley, Alec |
| contents | Belief Propagation (BP) is an efficient message-passing algorithm widely used for inference in graphical models and for solving various problems in statistical physics. However, BP often yields inaccurate estimates of order parameters and their susceptibilities in finite systems, particularly in sparse networks with few loops. Here, we show for both percolation and Ising models that fixing the state of a single well-connected "source" node to break global symmetry substantially improves inference accuracy and captures finite-size effects across a broad range of networks, especially tree-like ones, at no additional computational cost. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_19231 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Belief propagation for finite networks using a symmetry-breaking source node Kim, Seongmin Kirkley, Alec Social and Information Networks Statistical Mechanics Belief Propagation (BP) is an efficient message-passing algorithm widely used for inference in graphical models and for solving various problems in statistical physics. However, BP often yields inaccurate estimates of order parameters and their susceptibilities in finite systems, particularly in sparse networks with few loops. Here, we show for both percolation and Ising models that fixing the state of a single well-connected "source" node to break global symmetry substantially improves inference accuracy and captures finite-size effects across a broad range of networks, especially tree-like ones, at no additional computational cost. |
| title | Belief propagation for finite networks using a symmetry-breaking source node |
| topic | Social and Information Networks Statistical Mechanics |
| url | https://arxiv.org/abs/2510.19231 |