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Temporal graphs are commonly used to represent complex systems and track theevolution of their constituents over time. Visualizing these graphs is crucialas it allows one to quickly identify anomalies, trends, patterns, and otherproperties leading to better decision-making. In this context, theto-be-adopted temporal resolution is crucial in constructing and analyzing thelayout visually. The choice of a resolution is critical, e.g., when dealingwith temporally sparse graphs. In such cases, changing the temporal resolutionby grouping events (i.e., edges) from consecutive timestamps, a technique knownas timeslicing, can aid in the analysis and reveal patterns that might not bediscernible otherwise. However, choosing a suitable temporal resolution is nottrivial. In this paper, we propose TDANetVis, a methodology that suggeststemporal resolutions potentially relevant for analyzing a given graph, i.e.,resolutions that lead to substantial topological changes in the graphstructure. To achieve this goal, TDANetVis leverages zigzag persistenthomology, a well-established technique from Topological Data Analysis (TDA). Toenhance visual graph analysis, TDANetVis also incorporates the colored barcode,a novel timeline-based visualization built on the persistence barcodes commonlyused in TDA. We demonstrate the usefulness and effectiveness of TDANetVisthrough a usage scenario and a user study involving 27 participants.

TDANetVis: Suggesting temporal resolutions for graph visualization using zigzag persistent homology