Visualizing Communities in Dynamic Multivariate Networks

Karen Larkina, Oksana Holomsha, Lucas Lemos, Amílcar Soares, Rafael Messias Martins, Andreas Kerren, Vinicius R. P. Borges, Célia G. Ralha, Jean R. Ponciano, Claudio D. G. Linhares

Abstract: A dynamic (or temporal) network is a widely used structure that enables understanding dynamic systems by modeling interactions among system components over time. In many real-world cases, however, components (called nodes) and/or interactions (called edges) contain numerous meaningful attributes, leading to the need for a more suitable instrument for representing and analyzing these dynamic and complex systems with multiple attributes: the Dynamic Multivariate Network (DMVN). In this work, we extended LargeNetVis, a visualization system specifically designed for large dynamic networks that focus on network community structure and dynamics, to enable the visual exploration of DMVNs and their communities. The newly introduced visual encodings and interactions allow the visualization of nodes' and edges' attributes at different granularity levels and produce a node tracking capability from both top-down and bottom-up perspectives. With these functionalities, one can track individual nodes across dynamic communities over time. The proposed approach is validated by comparing it with the original LargeNetVis system and conducting a user evaluation involving 37 participants.

External IDs:dblp:conf/sibgrapi/LarkinaHLSMKBRPL25