Introducing multilayer stream graphs and layer centralities

TL;DR

This paper introduces multilayer stream graphs to better model complex, time-evolving interactions in data, and develops layer centralities to assess layer importance, demonstrated on large-scale datasets involving individuals and flights.

Contribution

It presents a novel multilayer stream graph model and new layer centrality measures, enhancing the analysis of dynamic, multi-faceted interaction data.

Findings

Layer centralities reveal subtle importance patterns in datasets.

Multilayer stream graphs capture complex temporal interactions.

Model explains nuanced behavior in real-world data.

Abstract

Graphs are commonly used in mathematics to represent some relationships between items. However, as simple objects, they sometimes fail to capture all relevant aspects of real-world data. To address this problem, we generalize them and model interactions over time with multilayer structure. We build and test several centralities to assess the importance of layers of such structures. In order to showcase the relevance of this new model with centralities, we give examples on two large-scale datasets of interactions, involving individuals and flights, and show that we are able to explain subtle behaviour patterns in both cases.