Problem
Research questions and friction points this paper is trying to address.
Time Graph Analysis
Optimal Time Segmentation
Complex System Dynamics
ZigzagNetVis: Suggesting temporal resolutions for graph visualization using zigzag persistence
🤖 AI Summary
To address the challenge of adaptively selecting temporal resolution in time-series graph visualization, this paper proposes an automated recommendation method based on zigzag persistent homology, which identifies optimal time granularity by detecting salient topological changes in graph structure. This work introduces zigzag persistent homology to the task of temporal resolution recommendation for time-series graphs for the first time. It further designs a novel timeline encoding—“colored barcodes”—to intuitively visualize multi-scale topological evolution. Additionally, a web-based prototype system supporting interactive exploration is implemented. In a user study involving 27 participants and quantitative evaluation, the method significantly improves efficiency in anomaly detection and pattern discovery for sparse time-series graphs, demonstrating advantages in both robustness and interpretability.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Zigzag Persistent Homology
Visualization Tool
Temporal Graph Analysis
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