Quantitative Evaluation of Snapshot Graphs for the Analysis of Temporal Networks

Alessandro Chiappori (1) , Rémy Cazabet (2, 3, 4, 5)

1 Phys-ENS - Laboratoire de Physique de l'ENS Lyon
2 DM2L - Data Mining and Machine Learning
3 LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
4 UCBL - Université Claude Bernard Lyon 1
5 IXXI - Institut Rhône-Alpin des systèmes complexes

One of the most common approaches to the analysis of dynamic networks is through time-window aggregation. The resulting representation is a sequence of static networks, i.e. the snapshot graph. Despite this representation being widely used in the literature, a general framework to evaluate the soundness of snapshot graphs is still missing. In this article, we propose two scores to quantify conflicting objectives: Stability measures how much stable the sequence of snapshots is, while Fidelity measures the loss of information compared to the original data. We also develop a technique of targeted filtering of the links, to simplify the original temporal network. Our framework is tested on datasets of proximity and face-to-face interactions.

Mots clés: temporal networks; dynamic networks; time-window aggregation; snapshot graphs; choice of the window size

Domaines: Réseaux sociaux et d'information [cs.SI]; Apprentissage [cs.LG]

Alessandro Chiappori, Rémy Cazabet. Quantitative Evaluation of Snapshot Graphs for the Analysis of Temporal Networks. COMPLEX NETWORKS 2021 - 10th International Conference on Complex Networks and their Applications, Nov 2021, Madrid, Spain. ⟨hal-03560917⟩