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  1. Book ; Online: Efficient distributed representations beyond negative sampling

    Dall'Amico, Lorenzo / Belliardo, Enrico Maria

    2023  

    Abstract: This article describes an efficient method to learn distributed representations, also known as embeddings. This is accomplished minimizing an objective function similar to the one introduced in the Word2Vec algorithm and later adopted in several works. ... ...

    Abstract This article describes an efficient method to learn distributed representations, also known as embeddings. This is accomplished minimizing an objective function similar to the one introduced in the Word2Vec algorithm and later adopted in several works. The optimization computational bottleneck is the calculation of the softmax normalization constants for which a number of operations scaling quadratically with the sample size is required. This complexity is unsuited for large datasets and negative sampling is a popular workaround, allowing one to obtain distributed representations in linear time with respect to the sample size. Negative sampling consists, however, in a change of the loss function and hence solves a different optimization problem from the one originally proposed. Our contribution is to show that the sotfmax normalization constants can be estimated in linear time, allowing us to design an efficient optimization strategy to learn distributed representations. We test our approximation on two popular applications related to word and node embeddings. The results evidence competing performance in terms of accuracy with respect to negative sampling with a remarkably lower computational time.
    Keywords Computer Science - Machine Learning ; Computer Science - Computation and Language ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2023-03-30
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Political context of the European vaccine debate on Twitter.

    Paoletti, Giordano / Dall'Amico, Lorenzo / Kalimeri, Kyriaki / Lenti, Jacopo / Mejova, Yelena / Paolotti, Daniela / Starnini, Michele / Tizzani, Michele

    Scientific reports

    2024  Volume 14, Issue 1, Page(s) 4397

    Abstract: At the beginning of the COVID-19 pandemic, fears grew that making vaccination a political (instead of public health) issue may impact the efficacy of this life-saving intervention, spurring the spread of vaccine-hesitant content. In this study, we ... ...

    Abstract At the beginning of the COVID-19 pandemic, fears grew that making vaccination a political (instead of public health) issue may impact the efficacy of this life-saving intervention, spurring the spread of vaccine-hesitant content. In this study, we examine whether there is a relationship between the political interest of social media users and their exposure to vaccine-hesitant content on Twitter. We focus on 17 European countries using a multilingual, longitudinal dataset of tweets spanning the period before COVID, up to the vaccine roll-out. We find that, in most countries, users' endorsement of vaccine-hesitant content is the highest in the early months of the pandemic, around the time of greatest scientific uncertainty. Further, users who follow politicians from right-wing parties, and those associated with authoritarian or anti-EU stances are more likely to endorse vaccine-hesitant content, whereas those following left-wing politicians, more pro-EU or liberal parties, are less likely. Somewhat surprisingly, politicians did not play an outsized role in the vaccine debates of their countries, receiving a similar number of retweets as other similarly popular users. This systematic, multi-country, longitudinal investigation of the connection of politics with vaccine hesitancy has important implications for public health policy and communication.
    MeSH term(s) Humans ; Pandemics ; Social Media ; Vaccines ; Vaccination ; Administrative Personnel
    Chemical Substances Vaccines
    Language English
    Publishing date 2024-02-22
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-024-54863-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Book ; Online: Nishimori meets Bethe

    Dall'Amico, Lorenzo / Couillet, Romain / Tremblay, Nicolas

    a spectral method for node classification in sparse weighted graphs

    2021  

    Abstract: This article unveils a new relation between the Nishimori temperature parametrizing a distribution P and the Bethe free energy on random Erdos-Renyi graphs with edge weights distributed according to P. Estimating the Nishimori temperature being a task of ...

    Abstract This article unveils a new relation between the Nishimori temperature parametrizing a distribution P and the Bethe free energy on random Erdos-Renyi graphs with edge weights distributed according to P. Estimating the Nishimori temperature being a task of major importance in Bayesian inference problems, as a practical corollary of this new relation, a numerical method is proposed to accurately estimate the Nishimori temperature from the eigenvalues of the Bethe Hessian matrix of the weighted graph. The algorithm, in turn, is used to propose a new spectral method for node classification in weighted (possibly sparse) graphs. The superiority of the method over competing state-of-the-art approaches is demonstrated both through theoretical arguments and real-world data experiments.
    Keywords Statistics - Machine Learning ; Condensed Matter - Statistical Mechanics ; Computer Science - Machine Learning
    Publishing date 2021-03-05
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Temporal network-based analysis of fluid flow with applications to marine ecology

    Acharya, Kishor / Aguilar, Javier / Dall'Amico, Lorenzo / Nicolaou, Kyriacos / Tong, Johnny / Ser-Giacomi, Enrico

    2023  

    Abstract: In this report we present the work carried out during the Complexity72h workshop, held at IFISC in Palma de Mallorca, Spain, 26-30 June 2023. We describe a temporal network-theoretic approach to study fluid flows with applications to marine ecology. The ... ...

    Abstract In this report we present the work carried out during the Complexity72h workshop, held at IFISC in Palma de Mallorca, Spain, 26-30 June 2023. We describe a temporal network-theoretic approach to study fluid flows with applications to marine ecology. The network representation is derived from the Lagrangian fluid dynamics and represents fluid transportation between patches of the sea. It is a directed, weighted and time-dependent network. This approach enables us to use advanced network-theoretic tools for analysis and modeling. A common approximation adopted in the literature consists in using an aggregated time-independent network representation of the fluid flow. In this report we focus in particular on the role played by the temporal component and to the information loss related to neglecting that dimension and inspect the role played by seasonal or long time-period variations. We conduct an analysis of basic network features of the aggregated and temporal graphs, we analyze their community structure and we model population dynamics of marine lives driven by the flow. Ultimately, we determine that time-independent approximations can effectively represent long-term transportation evolution spanning multiple years. However, for an accurate depiction of transportation within a single year, it is necessary to incorporate explicit time-dependence in the transport matrix to account for seasonality.
    Keywords Physics - Physics and Society ; Physics - Fluid Dynamics ; Quantitative Biology - Populations and Evolution
    Subject code 532
    Publishing date 2023-06-30
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Generalized contact matrices for epidemic modeling

    Manna, Adriana / Dall'Amico, Lorenzo / Tizzoni, Michele / Karsai, Marton / Perra, Nicola

    2023  

    Abstract: Contact matrices have become a key ingredient of modern epidemic models. They account for the stratification of contacts for the age of individuals and, in some cases, the context of their interactions. However, age and context are not the only factors ... ...

    Abstract Contact matrices have become a key ingredient of modern epidemic models. They account for the stratification of contacts for the age of individuals and, in some cases, the context of their interactions. However, age and context are not the only factors shaping contact structures and affecting the spreading of infectious diseases. Socio-economic status (SES) variables such as wealth, ethnicity, and education play a major role as well. Here, we introduce generalized contact matrices capable of stratifying contacts across any number of dimensions including any SES variable. We derive an analytical expression for the basic reproductive number of an infectious disease unfolding on a population characterized by such generalized contact matrices. Our results, on both synthetic and real data, show that disregarding higher levels of stratification might lead to the under-estimation of the reproductive number and to a mis-estimation of the global epidemic dynamics. Furthermore, including generalized contact matrices allows for more expressive epidemic models able to capture heterogeneities in behaviours such as different levels of adoption of non-pharmaceutical interventions across different groups. Overall, our work contributes to the literature attempting to bring socio-economic, as well as other dimensions, to the forefront of epidemic modeling. Tackling this issue is crucial for developing more precise descriptions of epidemics, and thus to design better strategies to contain them.
    Keywords Physics - Physics and Society ; Quantitative Biology - Populations and Evolution
    Subject code 612
    Publishing date 2023-06-29
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Estimating household contact matrices structure from easily collectable metadata.

    Dall'Amico, Lorenzo / Kleynhans, Jackie / Gauvin, Laetitia / Tizzoni, Michele / Ozella, Laura / Makhasi, Mvuyo / Wolter, Nicole / Language, Brigitte / Wagner, Ryan G / Cohen, Cheryl / Tempia, Stefano / Cattuto, Ciro

    PloS one

    2024  Volume 19, Issue 3, Page(s) e0296810

    Abstract: Contact matrices are a commonly adopted data representation, used to develop compartmental models for epidemic spreading, accounting for the contact heterogeneities across age groups. Their estimation, however, is generally time and effort consuming and ... ...

    Abstract Contact matrices are a commonly adopted data representation, used to develop compartmental models for epidemic spreading, accounting for the contact heterogeneities across age groups. Their estimation, however, is generally time and effort consuming and model-driven strategies to quantify the contacts are often needed. In this article we focus on household contact matrices, describing the contacts among the members of a family and develop a parametric model to describe them. This model combines demographic and easily quantifiable survey-based data and is tested on high resolution proximity data collected in two sites in South Africa. Given its simplicity and interpretability, we expect our method to be easily applied to other contexts as well and we identify relevant questions that need to be addressed during the data collection procedure.
    MeSH term(s) Metadata ; Surveys and Questionnaires ; Epidemics ; Epidemiological Models ; South Africa ; Contact Tracing/methods
    Language English
    Publishing date 2024-03-14
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0296810
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Book ; Online: Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian

    Dall'Amico, Lorenzo / Couillet, Romain / Tremblay, Nicolas

    2020  

    Abstract: This article considers the problem of community detection in sparse dynamical graphs in which the community structure evolves over time. A fast spectral algorithm based on an extension of the Bethe-Hessian matrix is proposed, which benefits from the ... ...

    Abstract This article considers the problem of community detection in sparse dynamical graphs in which the community structure evolves over time. A fast spectral algorithm based on an extension of the Bethe-Hessian matrix is proposed, which benefits from the positive correlation in the class labels and in their temporal evolution and is designed to be applicable to any dynamical graph with a community structure. Under the dynamical degree-corrected stochastic block model, in the case of two classes of equal size, we demonstrate and support with extensive simulations that our proposed algorithm is capable of making non-trivial community reconstruction as soon as theoretically possible, thereby reaching the optimal detectability threshold and provably outperforming competing spectral methods.
    Keywords Computer Science - Social and Information Networks ; Computer Science - Machine Learning ; Statistics - Machine Learning
    Publishing date 2020-06-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: A unified framework for spectral clustering in sparse graphs

    Dall'Amico, Lorenzo / Couillet, Romain / Tremblay, Nicolas

    2020  

    Abstract: This article considers spectral community detection in the regime of sparse networks with heterogeneous degree distributions, for which we devise an algorithm to efficiently retrieve communities. Specifically, we demonstrate that a conveniently ... ...

    Abstract This article considers spectral community detection in the regime of sparse networks with heterogeneous degree distributions, for which we devise an algorithm to efficiently retrieve communities. Specifically, we demonstrate that a conveniently parametrized form of regularized Laplacian matrix can be used to perform spectral clustering in sparse networks, without suffering from its degree heterogeneity. Besides, we exhibit important connections between this proposed matrix and the now popular non-backtracking matrix, the Bethe-Hessian matrix, as well as the standard Laplacian matrix. Interestingly, as opposed to competitive methods, our proposed improved parametrization inherently accounts for the hardness of the classification problem. These findings are summarized under the form of an algorithm capable of both estimating the number of communities and achieving high-quality community reconstruction.
    Keywords Statistics - Machine Learning ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2020-03-20
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Association of close-range contact patterns with SARS-CoV-2: a household transmission study.

    Kleynhans, Jackie / Dall'Amico, Lorenzo / Gauvin, Laetitia / Tizzoni, Michele / Maloma, Lucia / Walaza, Sibongile / Martinson, Neil A / von Gottberg, Anne / Wolter, Nicole / Makhasi, Mvuyo / Cohen, Cheryl / Cattuto, Ciro / Tempia, Stefano

    eLife

    2023  Volume 12

    Abstract: Background: Households are an important location for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission, especially during periods when travel and work was restricted to essential services. We aimed to assess the association of ... ...

    Abstract Background: Households are an important location for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission, especially during periods when travel and work was restricted to essential services. We aimed to assess the association of close-range contact patterns with SARS-CoV-2 transmission.
    Methods: We deployed proximity sensors for two weeks to measure face-to-face interactions between household members after SARS-CoV-2 was identified in the household, in South Africa, 2020-2021. We calculated the duration, frequency, and average duration of close-range proximity events with SARS-CoV-2 index cases. We assessed the association of contact parameters with SARS-CoV-2 transmission using mixed effects logistic regression accounting for index and household member characteristics.
    Results: We included 340 individuals (88 SARS-CoV-2 index cases and 252 household members). On multivariable analysis, factors associated with SARS-CoV-2 acquisition were index cases with minimum C
    Conclusions: We did not find an association between close-range proximity events and SARS-CoV-2 household transmission. Our findings may be due to study limitations, that droplet-mediated transmission during close-proximity contacts plays a smaller role than airborne transmission of SARS-CoV-2 in the household, or due to high contact rates in households.
    Funding: Wellcome Trust (Grant number 221003/Z/20/Z) in collaboration with the Foreign, Commonwealth, and Development Office, United Kingdom.
    MeSH term(s) Humans ; Female ; SARS-CoV-2 ; COVID-19/epidemiology ; Family Characteristics ; Travel ; South Africa/epidemiology
    Language English
    Publishing date 2023-07-18
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2687154-3
    ISSN 2050-084X ; 2050-084X
    ISSN (online) 2050-084X
    ISSN 2050-084X
    DOI 10.7554/eLife.84753
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Book ; Online: Optimal Laplacian regularization for sparse spectral community detection

    Dall'Amico, Lorenzo / Couillet, Romain / Tremblay, Nicolas

    2019  

    Abstract: Regularization of the classical Laplacian matrices was empirically shown to improve spectral clustering in sparse networks. It was observed that small regularizations are preferable, but this point was left as a heuristic argument. In this paper we ... ...

    Abstract Regularization of the classical Laplacian matrices was empirically shown to improve spectral clustering in sparse networks. It was observed that small regularizations are preferable, but this point was left as a heuristic argument. In this paper we formally determine a proper regularization which is intimately related to alternative state-of-the-art spectral techniques for sparse graphs.
    Keywords Computer Science - Machine Learning ; Statistics - Machine Learning
    Publishing date 2019-12-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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