Covariance and correlation estimators in bipartite complex systems with a double heterogeneity

Puccio, Elena; Vassallo, Pietro; Piilo, Jyrki; Tumminello, Michele

doi:10.1088/1742-5468/ab16c5

Cited by 3 publications

(2 citation statements)

References 37 publications

(73 reference statements)

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“…In recent years, many complex systems have been represented by bipartite networks [34][35][36] . The Statistically Validated Network, introduced by Tumminello et al 19 , is an unsupervised method to statistically test the significance of each link of a projected weighted network as obtained from a multipartite network.…”

Section: Statistically Validated Networkmentioning

confidence: 99%

Ranking coherence in topic models using statistically validated networks

Simonetti

Albano

Plaia

et al. 2023

Journal of Information Science

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Probabilistic topic models have become one of the most widespread machine learning techniques in textual analysis. Topic discovering is an unsupervised process that does not guarantee the interpretability of its output. Hence, the automatic evaluation of topic coherence has attracted the interest of many researchers over the last decade, and it is an open research area. This article offers a new quality evaluation method based on statistically validated networks (SVNs). The proposed probabilistic approach consists of representing each topic as a weighted network of its most probable words. The presence of a link between each pair of words is assessed by statistically validating their co-occurrence in sentences against the null hypothesis of random co-occurrence. The proposed method allows one to distinguish between high-quality and low-quality topics, by making use of a battery of statistical tests. The statistically significant pairwise associations of words represented by the links in the SVN might reasonably be expected to be strictly related to the semantic coherence and interpretability of a topic. Therefore, the more connected the network, the more coherent the topic in question. We demonstrate the effectiveness of the method through an analysis of a real text corpus, which shows that the proposed measure is more correlated with human judgement than the state-of-the-art coherence measures.

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Section: Statistically Validated Networkmentioning

confidence: 99%

Ranking coherence in topic models using statistically validated networks

Simonetti

Albano

Plaia

et al. 2023

Journal of Information Science

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“…A noteworthy implication is that the hypergeometric distribution ( 10) is a consistent, precise theoretical method for validating node bonds. Indeed, double heterogeneity requires more complex probability models, which are often more challenging from a computational point of view (Puccio et al, 2019;Tumminello et al, 2013).…”

Section: The Ivass Antifraud Integrated Archivementioning

confidence: 99%

Insurance fraud detection: A statistically validated network approach

Tumminello

Consiglio

Vassallo

et al. 2022

J of Risk & Insurance

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Fraud is a social phenomenon, and fraudsters often collaborate with other fraudsters, taking on different roles. The challenge for insurance companies is to implement claim assessment and improve fraud detection accuracy. We developed an investigative system based on bipartite networks, highlighting the relationships between subjects and accidents or vehicles and accidents. We formalize filtering rules through probability models and test specific methods to assess the existence of communities in extensive networks and propose new alert metrics for suspicious structures. We apply the methodology to a real database—the Italian Antifraud Integrated Archive—and compare the results to out‐of‐sample fraud scams under investigation by the judicial authorities.

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