| Full text | |
| Author(s): |
Tinarrage, Raphael
;
Ponciano, Jean R.
;
Linhares, Claudio D. G.
;
Traina, Agma J. M.
;
Poco, Jorge
Total Authors: 5
|
| Document type: | Journal article |
| Source: | IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS; v. 31, n. 10, p. 18-pg., 2025-10-01. |
| Abstract | |
Temporal graphs are commonly used to represent complex systems and track the evolution of their constituents over time. Visualizing these graphs is crucial as it allows one to quickly identify anomalies, trends, patterns, and other properties that facilitate better decision-making. In this context, selecting an appropriate temporal resolution is essential for constructing and visually analyzing the layout. The choice of resolution is particularly important, especially when dealing with temporally sparse graphs. In such cases, changing the temporal resolution by grouping events (i.e., edges) from consecutive timestamps - a technique known as timeslicing - can aid in the analysis and reveal patterns that might not be discernible otherwise. However, selecting an appropriate temporal resolution is a challenging task. In this paper, we propose ZigzagNetVis, a methodology that suggests temporal resolutions potentially relevant for analyzing a given graph, i.e., resolutions that lead to substantial topological changes in the graph structure. ZigzagNetVis achieves this by leveraging zigzag persistent homology, a well-established technique from Topological Data Analysis (TDA). To improve visual graph analysis, ZigzagNetVis incorporates the colored barcode, a novel timeline-based visualization inspired by persistence barcodes commonly used in TDA. We also contribute with a web-based system prototype that implements suggestion methodology and visualization tools. Finally, we demonstrate the usefulness and effectiveness of ZigzagNetVis through a usage scenario, a user study with 27 participants, and a detailed quantitative evaluation. (AU) | |
| FAPESP's process: | 22/13190-1 - Visualization of semantic and temporal networks to support understand medical and scientific data |
| Grantee: | Jean Roberto Ponciano |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 20/07200-9 - Analyzing complex data from COVID-19 to support decision making and prognosis |
| Grantee: | Agma Juci Machado Traina |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 23/18026-8 - Center for Data Science in Public Statistics |
| Grantee: | Carlos Eduardo Torres Freire |
| Support Opportunities: | Research Grants - Science Centers for Development |
| FAPESP's process: | 21/07012-0 - Data-driven intelligence for urban crime analysis and perception |
| Grantee: | Jorge Luis Poco Medina |
| Support Opportunities: | Research Grants - Young Investigators Grants |
| FAPESP's process: | 20/10049-0 - Complex networks and content-based image retrieval supported by selective visual attention features |
| Grantee: | Cláudio Douglas Gouveia Linhares |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 16/17078-0 - Mining, indexing and visualizing Big Data in clinical decision support systems (MIVisBD) |
| Grantee: | Agma Juci Machado Traina |
| Support Opportunities: | Research Projects - Thematic Grants |