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Email Based Institutional Network Analysis: Applications and Risks

dc.contributor.authorChristidis, Panayotis
dc.contributor.authorGómez Losada, Álvaro
dc.date.accessioned2024-03-08T06:43:02Z
dc.date.available2024-03-08T06:43:02Z
dc.date.issued2019-11-08
dc.identifier.citationChristidis P, Gomez Losada Á. Email Based Institutional Network Analysis: Applications and Risks. Social Sciences. 2019; 8(11):306. https://doi.org/10.3390/socsci8110306es
dc.identifier.issn2076-0760
dc.identifier.urihttps://hdl.handle.net/20.500.12412/5450
dc.description.abstractSocial Network Analysis can be applied to describe the patterns of communication within an organisation. We explore how extending standard methods, by accounting for the direction and volume of emails, can reveal information regarding the roles of individual members. We propose an approach that models certain operational aspects of the organization, based on directional and weighted indicators. The approach is transferable to other types of social network with asymmetrical connections among its members. However, its applicability is limited by privacy concerns, the existence of multiple alternative communication channels that evolve over time, the difficulty of establishing clear links between organisational structure and efficiency and, most importantly, the challenge of setting up a system that measures the impact of communication behavior without influencing the communication behaviour itself.es
dc.language.isoenges
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleEmail Based Institutional Network Analysis: Applications and Riskses
dc.typearticlees
dc.identifier.doi10.3390/socsci8110306
dc.issue.number11es
dc.journal.titleSocial Scienceses
dc.rights.accessRightsopenAccesses
dc.subject.keywordSocial network analysises
dc.subject.keywordEmail traffices
dc.subject.keywordCentralityes
dc.subject.keywordClosenesses
dc.subject.keywordClustering effectes
dc.subject.keywordGraph theoryes
dc.subject.keywordComplex networkses
dc.subject.keywordMachine learninges
dc.subject.keywordBig Dataes
dc.subject.keywordOrganisational dynamicses
dc.volume.number8es


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