TL;DR
This paper introduces temporal graphs as a way to encode and analyze time-dependent data using graph algorithms, providing new metrics and real-world applications.
Contribution
It presents the concept of temporal graphs, new metrics for their analysis, and demonstrates their utility on real-world data without requiring simulations.
Findings
Temporal graphs effectively encode temporal data.
New metrics enable analysis of dynamic properties.
Application to real-world data yields insightful results.
Abstract
We introduce the idea of temporal graphs, a representation that encodes temporal data into graphs while fully retaining the temporal information of the original data. This representation lets us explore the dynamic temporal properties of data by using existing graph algorithms (such as shortest-path), with no need for data-driven simulations. We also present a number of metrics that can be used to study and explore temporal graphs. Finally, we use temporal graphs to analyse real-world data and present the results of our analysis.
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