Alice and the Caterpillar: A more descriptive null model for assessing data mining results
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912424937914368 |
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| author | Preti, Giulia Morales, Gianmarco De Francisci Riondato, Matteo |
| author_facet | Preti, Giulia Morales, Gianmarco De Francisci Riondato, Matteo |
| contents | We introduce novel null models for assessing the results obtained from observed binary transactional and sequence datasets, using statistical hypothesis testing. Our null models maintain more properties of the observed dataset than existing ones. Specifically, they preserve the Bipartite Joint Degree Matrix of the bipartite (multi-)graph corresponding to the dataset, which ensures that the number of caterpillars, i.e., paths of length three, is preserved, in addition to other properties considered by other models. We describe Alice, a suite of Markov chain Monte Carlo algorithms for sampling datasets from our null models, based on a carefully defined set of states and efficient operations to move between them. The results of our experimental evaluation show that Alice mixes fast and scales well, and that our null model finds different significant results than ones previously considered in the literature. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_09764 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Alice and the Caterpillar: A more descriptive null model for assessing data mining results Preti, Giulia Morales, Gianmarco De Francisci Riondato, Matteo Social and Information Networks Machine Learning We introduce novel null models for assessing the results obtained from observed binary transactional and sequence datasets, using statistical hypothesis testing. Our null models maintain more properties of the observed dataset than existing ones. Specifically, they preserve the Bipartite Joint Degree Matrix of the bipartite (multi-)graph corresponding to the dataset, which ensures that the number of caterpillars, i.e., paths of length three, is preserved, in addition to other properties considered by other models. We describe Alice, a suite of Markov chain Monte Carlo algorithms for sampling datasets from our null models, based on a carefully defined set of states and efficient operations to move between them. The results of our experimental evaluation show that Alice mixes fast and scales well, and that our null model finds different significant results than ones previously considered in the literature. |
| title | Alice and the Caterpillar: A more descriptive null model for assessing data mining results |
| topic | Social and Information Networks Machine Learning |
| url | https://arxiv.org/abs/2506.09764 |