Information-theoretic metrics of higher-order interactions on graphs

Title:

Information-theoretic metrics of higher-order interactions on graphs



Abstract:

In this work, we contribute to the growing understanding of

higher-order interactions (those involving more than two variables),

in particular the distinction between higher-order mechanisms and

higher-order behaviors. We employ a newly-developed metric based on

information theory to detect the presence of higher-order behaviors

from time-series data. Our work comprises a thorough numerical study

of the behavior of this metric under different synthetic dynamics and

models for epidemic spreading on small graphs. The ultimate aim is

applying this methodology to identify higher-order mechanisms in

dynamics on complex networks, and in general to study the synergies

resulting from network effects. To this end, we also present

preliminary results on applying this metric on SIS dynamics on small

networks. The methods explored in this work have the potential to

illuminate our understanding of higher-order interactions, their

definition and importance, and thus of the nature of complex systems

in general.

Presential at IFISC's seminars room and online at https://us06web.zoom.us/j/84189947714?pwd=4xmMKs6rq3res6OGYEo6FotkW3D2Ca.1