Shannon entropy in time-varying semantic networks of titles of scientific paper
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Applied Network Science
RESEARCH
Open Access
Shannon entropy in time-varying semantic networks of titles of scientific paper Marcelo do Vale Cunha1,2*† , Carlos Cesar Ribeiro Santos1† , Marcelo Albano Moret1,3† and Hernane Borges de Barros Pereira1,3† *Correspondence: [email protected] † All authors contributed equally to this work. 1 Programa de Modelagem Computacional, Centro Universitário Senai Cimatec, Av. Orlando Gomes, 1845, 41650-010 Salvador, Brasil 2 Departamento de Ensino, Instituto Federal da Bahia, R. Gileno de Sá Oliveira, 271 - Recanto dos Pássaros, 47808-006 Barreiras, Brasil Full list of author information is available at the end of the article
Abstract Recent work has employed information theory in social and complex networks. Studies often discuss entropy in the degree distributions of a network. However, no specific work on entropy exists in clique networks. This work is an extension of a previous study that discussed this topic. We propose a method for calculating the entropy of a clique network and its minimum and maximum values in temporal semantic networks based on titles of scientific papers. In addition, the critical network of moments was extracted. We use the titles of scientific papers published in Nature and Science over ten-year period. The results show the diversity of vocabulary over time, based on the entropy values of vertices and edges. In each critical network, we discover the paths that connect important words and an interesting modular structure. Keywords: Networks of cliques, Shannon entropy, Time–varying graphs, Semantic networks, Network theory
Introduction Information theory has evolved in recent decades and has been applied in different fields, such as biology, economics and quantum confined systems (Mousavian et al. 2016; Mishra and Ayyub 2019; Nascimento and Prudente 2018; Brillouin 2013). Recently, some authors have introduced these concepts to measure the information contained in the distribution of degrees and geodesic distances from real networks, or in classical models and semantic networks to classify and differentiate these systems by the heterogeneity of their links (Solé and Valverde 2004; Ji et al. 2008; Viol et al. 2019). In the study of real networks, modeling the dynamics of the entry and exit of vertices and edges of the networks is necessary. The main models include the modeling of a system by a clique network, e.g., movie actor networks (Barabasi and Albert 1999), co–authoring networks (Newman 2001), concepts networks (Caldeira et al. 2006) and semantic networks (Teixeira et al. 2010; Pereira et al. 2011; Pereira et al. 2016; Grilo et al. 2017). The latter considers the network that is composed of words, concepts or entities with semantic meaning represented by the vertices, with edges that consist of connections between two words that appear in the same unit of meaning, that is, in a sentence (phrase), paragraph or title of the analyzed speech (Pereira et al. 2016; Grilo et al. 2017). Semantic networks © The Author(s). 2020 Open Access This artic
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