Social network analysis of mythology field

PurposeThe main purpose of the study is to detect, monitor the mythology field and make predictions of the development of it using social network analysis metrics. Mythology, which is the subject of many disciplines, is an area with extensive working potential. In addition to basic bibliometric indicators, the relationships of this field, which cannot be seen by other methods, were analyzed using measures such as centrality, between, eigenvector, modularity and silhouette coefficients.Design/methodology/approachIn this study, social network analysis of the field of mythology, which has an interdisciplinary structure, was made. Within the scope of the study, 28,370 publications were selected from the publications in the field of mythology in the Web of Science (WoS) citation database between 1900 and 2019 using the probability-based stratified sampling method (5%), and detailed analyzes were made on these publications. The aforementioned publications were analyzed in terms of publication and citation numbers, publication types, subject categories, keywords used, co-authorship, researchers with the highest number of publications, institutions and countries with the highest number of document co-citations.FindingsThe findings show that the field of mythology gathers around four main subjects (sociology, folklore, politics and anthropology). When interpreted in terms of centrality metrics in more detail, the symbiotic or complementary relationship between anthropology, folklore, politics, sociology and mythology can be easily observed.Originality/valueThe findings of this study are seen important for scientists, decision-makers and policymakers. In addition, the findings of the study can be used to create the curriculum of the field.

On the equivalence between bimodal and unimodal students’ collaboration networks in distance learning

This work is aiming to contribute to the field of Distance Learning through Learning Analytics. We propose a methodological framework based on network analysis metrics to provide multiple indicators for Course Learning Analytics. Social Network Analysis is proposed for this purpose due to its capacity to provide an integrated representation of students’ interaction, where individual behavior is expressed within the context of a learning community. We perform experimental evaluation on real-life data from anonymized forum posts of postgraduate students and their tutors in the School of Science and Technology at the Hellenic Open University. Initially, we create and examine two-mode networks (participant-discussion) for two different modules. Subsequently, these networks are transformed into one-mode networks. Key measures are estimated and compared and the differences between their pedagogical interpretations are highlighted. We conclude that the choice between working with a bimodal network or projecting it into a unimodal one is determined by the nature of the research questions because of the distinct features that each one of them exhibits.

POLITICAL FANDOMS AND SUPERPARTICIPANTS IN POLITICAL CONVERSATIONS ON TWITTERTwitter, social network analysis, political polarization, fandoms

In this proposal, we discuss the role of superparticipants in political conversations on Twitter. Our hypothesis is that these highly active users show a clear political position and intentionally act to give visibility to some topics and to reduce the visibility of others, practices that are similar to those observed among fans in popular culture. In terms of methods, we use social network analysis metrics to identify the modularity of the network and users that receive more attention than others (higher indegree) or mention more other users (higher outdegree). We collected tweets related to the impeachment of the Brazilian ex-president Dilma Rousseff in 2016 in three critical dates of the process. By observing the users with higher outdegree in each network, we noticed some patterns and behaviors that can characterize those users as political fans. Our main finding is that the superparticipants with higher outdegree helped to shape the polarized networks by retweeting like-minded accounts, and thus are important and influence the study of polarized political networks on Twitter.

ChemBioServer 2.0: an advanced web server for filtering, clustering and networking of chemical compounds facilitating both drug discovery and repurposing

Summary ChemBioServer 2.0 is the advanced sequel of a web server for filtering, clustering and networking of chemical compound libraries facilitating both drug discovery and repurposing. It provides researchers the ability to (i) browse and visualize compounds along with their physicochemical and toxicity properties, (ii) perform property-based filtering of compounds, (iii) explore compound libraries for lead optimization based on perfect match substructure search, (iv) re-rank virtual screening results to achieve selectivity for a protein of interest against different protein members of the same family, selecting only those compounds that score high for the protein of interest, (v) perform clustering among the compounds based on their physicochemical properties providing representative compounds for each cluster, (vi) construct and visualize a structural similarity network of compounds providing a set of network analysis metrics, (vii) combine a given set of compounds with a reference set of compounds into a single structural similarity network providing the opportunity to infer drug repurposing due to transitivity, (viii) remove compounds from a network based on their similarity with unwanted substances (e.g. failed drugs) and (ix) build custom compound mining pipelines. Availability and implementation http://chembioserver.vi-seem.eu.

Review on supply chain network metrics

Recent studies focused on the importance of adopting network analysis approaches such as social network analysis in the supply chain networks to better understand and manage the roles of organizations in inter-organizational relationships. The main aim of this research is to identify and integrate network analysis metrics in the existent literature in this realm which is applicable to characterize the position and role of organizations in the supply chain network context and their impact on the behavior and outcomes of organizations and the whole supply chain network. To this aim, we followed a systematic literature review process using Scopus database to identify high-quality papers through several screening stages. Our findings illustrate that there are two main sources of interfirm differences including atomistic properties and relational properties. With an emphasis on relational properties through the lens of network analysis metrics, we integrated influential characteristics on actor’s behavior and performance into three main categories of node level, tie level, and network level. Our findings are applicable to address any emergent phenomenon and the roles of actors based on their position in the network context such as supply chain network and study their behavior and performance.

Multivariate Data Envelopment Analysis to Measure Airline Efficiency in European Airspace: A Network-Based Approach

In this paper, data envelopment analysis (DEA) is applied to exhaustively examine the efficiency of the main airline companies in the European airspace by using novel input/output parameters: business management factors, network analysis metrics, as well as social media estimators. Furthermore, we also use network analysis to provide a better differentiation among efficiency values. Results indicate that user engagement, as well as the analysis of the position within the airspace-from an operative perspective, influence the efficiency of the airline companies, allowing a more comprehensive understanding of its functioning.

ChemBioServer 2.0: An Advanced Web Server for Filtering, Clustering and Networking of Chemical Compounds Facilitating Both Drug Discovery and Repurposing

<p>ChemBioServer 2.0 is the advanced sequel of a web-server for filtering, clustering and networking of chemical compound libraries facilitating both drug discovery and repurposing. It provides researchers the ability to (i) browse and visualize compounds along with their physicochemical and toxicity properties, (ii) perform property-based filtering of chemical compounds, (iii) explore compound libraries for lead optimization based on perfect match substructure search, (iv) re-rank virtual screening results to achieve selectivity for a protein of interest against different protein members of the same family, selecting only those compounds that score high for the protein of interest, (v) perform clustering among the compounds based on their physicochemical properties providing representative compounds for each cluster, (vi) construct and visualize a structural similarity network of compounds providing a set of network analysis metrics, (vii) combine a given set of compounds with a reference set of compounds into a single structural similarity network providing the opportunity to infer drug repurposing due to transitivity, (viii) remove compounds from a network based on their similarity with unwanted substances (e.g. failed drugs) and (ix) build custom compound mining pipelines. The updated web server is available in the URL: <a href="http://chembioserver.vi-seem.eu/">http://chembioserver.vi-seem.eu/</a> </p>

ChemBioServer 2.0: An Advanced Web Server for Filtering, Clustering and Networking of Chemical Compounds Facilitating Both Drug Discovery and Repurposing

<p>ChemBioServer 2.0 is the advanced sequel of a web-server for filtering, clustering and networking of chemical compound libraries facilitating both drug discovery and repurposing. It provides researchers the ability to (i) browse and visualize compounds along with their physicochemical and toxicity properties, (ii) perform property-based filtering of chemical compounds, (iii) explore compound libraries for lead optimization based on perfect match substructure search, (iv) re-rank virtual screening results to achieve selectivity for a protein of interest against different protein members of the same family, selecting only those compounds that score high for the protein of interest, (v) perform clustering among the compounds based on their physicochemical properties providing representative compounds for each cluster, (vi) construct and visualize a structural similarity network of compounds providing a set of network analysis metrics, (vii) combine a given set of compounds with a reference set of compounds into a single structural similarity network providing the opportunity to infer drug repurposing due to transitivity, (viii) remove compounds from a network based on their similarity with unwanted substances (e.g. failed drugs) and (ix) build custom compound mining pipelines. The updated web server is available in the URL: <a href="http://chembioserver.vi-seem.eu/">http://chembioserver.vi-seem.eu/</a> </p>

Using Social Network Analysis and Social Capital to Identify User Roles on Polarized Political Conversations on Twitter

In this article, we discuss the roles users play in political conversations on Twitter. Our case study is based on data collected in three dates during the former Brazilian president Lula’s corruption trial. We used a combination of social network analysis metrics and social capital to identify the users’ roles during polarized discussions that took place in each of the dates analyzed. Our research identified four roles, each associated with different aspects of social capital and social network metrics: activists, news clippers, opinion leaders, and information influencers. These roles are particularly useful to understand how users’ actions on political conversations may influence the structure of information flows.