A Gaussian integral that counts regular graphs
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866914937117343744 |
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| author | Evnin, Oleg Horinouchi, Weerawit |
| author_facet | Evnin, Oleg Horinouchi, Weerawit |
| contents | In a recent article J. Phys. Compl. 4 (2023) 035005, Kawamoto evoked statistical physics methods for the problem of counting graphs with a prescribed degree sequence. This treatment involved truncating a particular Taylor expansion at the first two terms, which resulted in the Bender-Canfield estimate for the graph counts. This is surprisingly successful since the Bender-Canfield formula is asymptotically accurate for large graphs, while the series truncation does not a priori suggest a similar level of accuracy.
We upgrade the above treatment in three directions. First, we derive an exact formula for counting d-regular graphs in terms of a d-dimensional Gaussian integral. Second, we show how to convert this formula into an integral representation for the generating function of d-regular graph counts. Third, we perform explicit saddle point analysis for large graph sizes and identify the saddle point configurations responsible for graph count estimates. In these saddle point configurations, only two of the integration variables condense to significant values, while the remaining ones approach zero for large graphs. This provides an underlying picture that justifies Kawamoto's earlier findings. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_04242 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Gaussian integral that counts regular graphs Evnin, Oleg Horinouchi, Weerawit Statistical Mechanics Disordered Systems and Neural Networks High Energy Physics - Theory Mathematical Physics Combinatorics In a recent article J. Phys. Compl. 4 (2023) 035005, Kawamoto evoked statistical physics methods for the problem of counting graphs with a prescribed degree sequence. This treatment involved truncating a particular Taylor expansion at the first two terms, which resulted in the Bender-Canfield estimate for the graph counts. This is surprisingly successful since the Bender-Canfield formula is asymptotically accurate for large graphs, while the series truncation does not a priori suggest a similar level of accuracy. We upgrade the above treatment in three directions. First, we derive an exact formula for counting d-regular graphs in terms of a d-dimensional Gaussian integral. Second, we show how to convert this formula into an integral representation for the generating function of d-regular graph counts. Third, we perform explicit saddle point analysis for large graph sizes and identify the saddle point configurations responsible for graph count estimates. In these saddle point configurations, only two of the integration variables condense to significant values, while the remaining ones approach zero for large graphs. This provides an underlying picture that justifies Kawamoto's earlier findings. |
| title | A Gaussian integral that counts regular graphs |
| topic | Statistical Mechanics Disordered Systems and Neural Networks High Energy Physics - Theory Mathematical Physics Combinatorics |
| url | https://arxiv.org/abs/2403.04242 |