Tie strength allows to classify social relationships and identify different types of them. For instance, social relationships can be classified as persistent and similar based respectively on the regularity with which they occur and the similarity among them. On the other hand, rare and somewhat similar relationships are random and cause noise in a social network, thus hiding the actual structure of the network and preventing an accurate analysis of it. In this article, we propose a method to handle social network data that exploits temporal features to improve the detection of communities by existing algorithms. By removing random relationships, we observe that social networks converge to a topology with more pure social relationships and better quality community structures.
In view of current COVID-19 pandemic scenario, uncertainties still remains around the role of domestic animals on COVID-19 epidemiology, this systematic review aims to present the scientific evidence available, so far, on dogs and cats epidemiological role in the COVID-19 pandemic. The systematic review was conducted in accordance with the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses – PRISMA. Selected studies reports negative results of dogs and cats both in serological tests, for the detection of neutralizing antibodies, and in tests that investigated the presence of the SARS-CoV2 RNA. We verified the lack of national studies, which serves as a motivation for more research to be carried out in Brazil. In Brazil, in the animals tested, 5.4 and 6.2% of cats and dogs were positive, respectively. Few studies, so far, have been carried out with stray animals or animals belonging to shelters. This absence of broad conducted studies on animal interface enphasizes the need for more research with animals in vulnerable conditions, since they can contribute to the spread of the disease if they are confirmed as positive for SARS-COV-2. The compilation of data, through this systematic review of the literature, to date suggests that dogs and cats are not factors of viral spread to humans. However, further studies are still recommended.
Relacionamentos sociais podem ser separados em diferentes classes pela regularidade com que ocorrem e pela similaridade entre eles. Neste contexto, propomos um processo para tratamento de dados de redes sociais que explora as características temporais para melhorar a detecção de comunidades por algoritmos existentes. Por meio de um processo de remoção de interações aleatórias, observamos que as redes sociais convergem para uma topologia com interações mais puramente sociais e comunidades com maior modularidade.
Community detection is a key task to further understand the function and the structure of complex networks. Therefore, a strategy used to assess this task must be able to avoid biased and incorrect results that might invalidate further analyses or applications that rely on such communities. Two widely used strategies to assess this task are generally known as structural and functional. The structural strategy basically consists in detecting and assessing such communities by using multiple methods and structural metrics. On the other hand, the functional strategy might be used when ground truth data are available to assess the detected communities. However, the evaluation of communities based on such strategies is usually done in experimental configurations that are largely susceptible to biases, a situation that is inherent to algorithms, metrics and network data used in this task. Furthermore, such strategies are not systematically combined in a way that allows for the identification and mitigation of bias in the algorithms, metrics or network data to converge into more consistent results. In this context, the main contribution of this article is an approach that supports a robust quality evaluation when detecting communities in real-world networks. In our approach, we measure the quality of a community by applying the structural and functional strategies, and the combination of both, to obtain different pieces of evidence. Then, we consider the divergences and the consensus among the pieces of evidence to identify and overcome possible sources of bias in community detection algorithms, evaluation metrics, and network data. Experiments conducted with several real and synthetic networks provided results that show the effectiveness of our approach to obtain more consistent conclusions about the quality of the detected communities.
Community detection is key to understand the structure of complex networks. However, the lack of appropriate evaluation strategies for this specific task may produce biased and incorrect results that might invalidate further analyses or applications based on such networks. In this context, the main contribution of this paper is an approach that supports a robust quality evaluation when detecting communities in real-world networks. In our approach, we use multiple strategies that capture distinct aspects of the communities. The conclusion on the quality of these communities is based on the consensus among the strategies adopted for the structural evaluation, as well as on the comparison with communities detected by different methods and with their existing ground truths. In this way, our approach allows one to overcome biases in network data, detection algorithms and evaluation metrics, thus providing more consistent conclusions about the quality of the detected communities. Experiments conducted with several real and synthetic networks provided results that show the effectiveness of our approach.
Os objetivos do presente estudo foram: contextualizar alunos da graduação acerca da temática bioinformática; propor estratégias de condução de projetos de Iniciação Científica em tempos de pandemia pelo novo coronavírus, possibilitando a realização de pesquisas no formato remoto. Esta pesquisa trata-se de um estudo observacional, transversal e descritivo realizado nos anos de 2020 e 2021, com a participação de alunos de graduação e do curso técnico das áreas de Ciências Agrárias e Sistemas de Informação, sendo 2 bolsistas PIBIC da graduação, 2 bolsistas PIBIC-EM do curso técnico para o projeto e alunos voluntários. No presente trabalho adotaram-se as estratégias metodológicas a seguir: aplicação e seleção dos alunos bolsista; definição de temas; oficinas temáticas teórico-práticas; revisão sistemática de literatura e; análises de bioinformática, utilizando-se as plataformas STITCH e CYTOSCAPE. Elaboração de trabalho científico para publicação e/ou apresentação em evento. Diante do atual cenário de pandemia, as atividades presenciais foram interrompidas. Assim realizados encontros de forma virtual, em que houve uma leitura prévia de reportagens e matérias acerca dos temas e, uma discussão baseada no material coletado. O senso crítico dos acadêmicos foi constantemente explorado. Nas análises bioinformática, os alunos conseguiram elaborar e interpretar diversas redes de interação químico-proteína utilizando como entradas na plataforma STITCH alguns compostos químicos das plantas Cagaiteira, Alecrim-pimenta e Neem e diferentes espécies. Conclui-se que projetos de pesquisa que são conduzidos por meio de análises de bioinformática, são de extrema importância para os alunos de Iniciação Científica em tempos de isolamento social em meio à pandemia da COVID-19.
A quantidade e os tipos de relacionamento que os membros de uma rede social estabelecem sugerem o perfil que cada indivíduo desenvolve ao longo do tempo. Neste artigo, propomos um método de mineração capaz de identificar e recuperar perfis sociais a partir de aspectos topológicos em redes temporais. A aplicação desse método permite identificar perfis persistentes e explicitar padrões que sugerem modelos de relacionamento em redes sociais.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.