Visualization of evolving social networks using actor-level and community-level trajectories

Detalhes bibliográficos
Autor(a) principal: Márcia Barbosa Oliveira
Data de Publicação: 2013
Outros Autores: João Gama
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://repositorio.inesctec.pt/handle/123456789/5357
http://dx.doi.org/10.1111/exsy.12028
Resumo: Visualization of static social networks is a mature research field in information visualization. Conventional approaches rely on node-link diagrams that provide a representation of the network topology by representing nodes as points and links between them as lines. However, the increasing availability of longitudinal network data has spurred interest in visualization techniques that go beyond the static node-link representation of a network. In temporal settings, the focus is on the network dynamics at different levels of analysis (e.g. node, communities and whole network). Yet, the development of visualizations that are able to provide actionable insights into different types of changes occurring in the network and their impact on both the neighbourhood and the overall network structure is a challenging task. In such settings, traditional node-link representations can prove to be limited (Yi et al., 2010). Alternative methods, such as matrix graph representations, fail in tasks involving path finding (Ghoniem et al., 2005). This work attempts to overcome these issues by proposing a methodology for tracking the evolution of dynamic social networks, at both the node-level and the community-level, based on the concept of temporal trajectory. We resort to three-order tensors to represent evolving social networks, and we further decompose them using a Tucker3 model. The two most representative components of this model define the 2D space where the trajectories of social entities are projected. To illustrate the proposed methodology, we conduct a case study using a set of temporal self-reported friendship networks.
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spelling Visualization of evolving social networks using actor-level and community-level trajectoriesVisualization of static social networks is a mature research field in information visualization. Conventional approaches rely on node-link diagrams that provide a representation of the network topology by representing nodes as points and links between them as lines. However, the increasing availability of longitudinal network data has spurred interest in visualization techniques that go beyond the static node-link representation of a network. In temporal settings, the focus is on the network dynamics at different levels of analysis (e.g. node, communities and whole network). Yet, the development of visualizations that are able to provide actionable insights into different types of changes occurring in the network and their impact on both the neighbourhood and the overall network structure is a challenging task. In such settings, traditional node-link representations can prove to be limited (Yi et al., 2010). Alternative methods, such as matrix graph representations, fail in tasks involving path finding (Ghoniem et al., 2005). This work attempts to overcome these issues by proposing a methodology for tracking the evolution of dynamic social networks, at both the node-level and the community-level, based on the concept of temporal trajectory. We resort to three-order tensors to represent evolving social networks, and we further decompose them using a Tucker3 model. The two most representative components of this model define the 2D space where the trajectories of social entities are projected. To illustrate the proposed methodology, we conduct a case study using a set of temporal self-reported friendship networks.2018-01-03T10:38:32Z2013-01-01T00:00:00Z2013info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/5357http://dx.doi.org/10.1111/exsy.12028engMárcia Barbosa OliveiraJoão Gamainfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-15T10:20:56Zoai:repositorio.inesctec.pt:123456789/5357Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:53:48.439826Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Visualization of evolving social networks using actor-level and community-level trajectories
title Visualization of evolving social networks using actor-level and community-level trajectories
spellingShingle Visualization of evolving social networks using actor-level and community-level trajectories
Márcia Barbosa Oliveira
title_short Visualization of evolving social networks using actor-level and community-level trajectories
title_full Visualization of evolving social networks using actor-level and community-level trajectories
title_fullStr Visualization of evolving social networks using actor-level and community-level trajectories
title_full_unstemmed Visualization of evolving social networks using actor-level and community-level trajectories
title_sort Visualization of evolving social networks using actor-level and community-level trajectories
author Márcia Barbosa Oliveira
author_facet Márcia Barbosa Oliveira
João Gama
author_role author
author2 João Gama
author2_role author
dc.contributor.author.fl_str_mv Márcia Barbosa Oliveira
João Gama
description Visualization of static social networks is a mature research field in information visualization. Conventional approaches rely on node-link diagrams that provide a representation of the network topology by representing nodes as points and links between them as lines. However, the increasing availability of longitudinal network data has spurred interest in visualization techniques that go beyond the static node-link representation of a network. In temporal settings, the focus is on the network dynamics at different levels of analysis (e.g. node, communities and whole network). Yet, the development of visualizations that are able to provide actionable insights into different types of changes occurring in the network and their impact on both the neighbourhood and the overall network structure is a challenging task. In such settings, traditional node-link representations can prove to be limited (Yi et al., 2010). Alternative methods, such as matrix graph representations, fail in tasks involving path finding (Ghoniem et al., 2005). This work attempts to overcome these issues by proposing a methodology for tracking the evolution of dynamic social networks, at both the node-level and the community-level, based on the concept of temporal trajectory. We resort to three-order tensors to represent evolving social networks, and we further decompose them using a Tucker3 model. The two most representative components of this model define the 2D space where the trajectories of social entities are projected. To illustrate the proposed methodology, we conduct a case study using a set of temporal self-reported friendship networks.
publishDate 2013
dc.date.none.fl_str_mv 2013-01-01T00:00:00Z
2013
2018-01-03T10:38:32Z
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http://dx.doi.org/10.1111/exsy.12028
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http://dx.doi.org/10.1111/exsy.12028
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