2021/10/26 by Alessandro Chiappori, Chiappori, Alessandro, Rémy Cazabet +1
Computer Science · Physics and Astronomy · Social Sciences · #Advanced Graph Neural Networks #Complex Network Analysis Techniques #Databases (cs.DB) #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Opportunistic and Delay-Tolerant Networks #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2110.13466
openalex publication_date 2021/10/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
One of the most common approaches to the analysis of dynamic networks is\nthrough time-window aggregation. The resulting representation is a sequence of\nstatic networks, i.e. the snapshot graph. Despite this representation being\nwidely used in the literature, a general framework to evaluate the soundness of\nsnapshot graphs is still missing. In this article, we propose two scores to\nquantify conflicting objectives: Stability measures how much stable the\nsequence of snapshots is, while Fidelity measures the loss of information\ncompared to the original data. We also develop a technique of targeted\nfiltering of the links, to simplify the original temporal network. Our\nframework is tested on datasets of proximity and face-to-face interactions.\n