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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2021
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2106.07106 |
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| _version_ | 1866910318812200960 |
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| author | Yi, Bongsoo O'Connor, Kevin McGoff, Kevin Nobel, Andrew B. |
| author_facet | Yi, Bongsoo O'Connor, Kevin McGoff, Kevin Nobel, Andrew B. |
| contents | We describe and study a transport based procedure called NetOTC (network optimal transition coupling) for the comparison and alignment of two networks. The networks of interest may be directed or undirected, weighted or unweighted, and may have distinct vertex sets of different sizes. Given two networks and a cost function relating their vertices, NetOTC finds a transition coupling of their associated random walks having minimum expected cost. The minimizing cost quantifies the difference between the networks, while the optimal transport plan itself provides alignments of both the vertices and the edges of the two networks. Coupling of the full random walks, rather than their marginal distributions, ensures that NetOTC captures local and global information about the networks, and preserves edges. NetOTC has no free parameters, and does not rely on randomization. We investigate a number of theoretical properties of NetOTC and present experiments establishing its empirical performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2106_07106 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Alignment and Comparison of Directed Networks via Transition Couplings of Random Walks Yi, Bongsoo O'Connor, Kevin McGoff, Kevin Nobel, Andrew B. Machine Learning We describe and study a transport based procedure called NetOTC (network optimal transition coupling) for the comparison and alignment of two networks. The networks of interest may be directed or undirected, weighted or unweighted, and may have distinct vertex sets of different sizes. Given two networks and a cost function relating their vertices, NetOTC finds a transition coupling of their associated random walks having minimum expected cost. The minimizing cost quantifies the difference between the networks, while the optimal transport plan itself provides alignments of both the vertices and the edges of the two networks. Coupling of the full random walks, rather than their marginal distributions, ensures that NetOTC captures local and global information about the networks, and preserves edges. NetOTC has no free parameters, and does not rely on randomization. We investigate a number of theoretical properties of NetOTC and present experiments establishing its empirical performance. |
| title | Alignment and Comparison of Directed Networks via Transition Couplings of Random Walks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2106.07106 |