A Temporal Network Visualization and Data Analysis on Two Decades of SBSI

• Lucas C. Lemos UnB
• Celia Ghedini Ralha UnB
• Daniela Claro UFBA
• Rita Suzana Pitangueira Maciel UFBA
• Aline Argolo UFBA
• Claudio D. G. Linhares Linnaeus University

Resumo

Context: The Brazilian Information Systems (BIS) community has gained importance in national and international scenarios. Understanding its evolution aids in defining contributions to future research. Problem: Gaining knowledge of large communities is not trivial, involving collecting adequate data, analyzing networks’ temporal behavior, and exploring different techniques of textual, graphical, and classification of structural evolution. Solution: Using two decades of SBSI publications provides an overview of collaboration networks’ temporal behavior to understand community evolution. IS Theory: This work is under social network theory to understand relationships between networks, links, or connections to identify interaction processes among authors and networks. Method: We conducted an exploratory study with a quantitative and qualitative approach to identify perspectives of the SBSI community phenomena as patterns, trends, and authors’ relationships in the networks. Summary of Results: The findings with 20 years include a network with 3,511 nodes, 3,610 relationships, and 771 detected communities, with an average Louvain modularity of 0.9559 indicating a solid community structure within the network but few connections where only 12 communities publish frequently over the two decades. Also, dividing 20 years by four timeslices, there are 104 community births, 20 grows, 31 contractions, and 331 deaths. Contributions and Impact in IS area: This work provides temporal BIS community behavior analysis using novel visualization techniques with structural, temporal, and evolution taxonomies to gain knowledge for decision-making for sustainable community growth.

Palavras-chave: Brazilian Information Systems Community, Community Detection, Community Taxonomies, Information Systems, LargeNetVis, SBSI, Temporal Network Visualization