Modeling memory in time-respecting paths on temporal networks

TL;DR

This paper introduces a framework to quantify and model memory effects in time-respecting paths on temporal networks, revealing strong memory influences that slow down spreading processes across various human proximity datasets.

Contribution

It presents a novel method to measure memory in temporal paths and a generative model to simulate memory effects in synthetic networks.

Findings

Memory effects are robust across datasets and parameters.

Memory significantly slows down diffusion processes.

Memoryless models underestimate spreading speeds.

Abstract

Human close-range proximity interactions are the key determinant for spreading processes like knowledge diffusion, norm adoption, and infectious disease transmission. These dynamical processes can be modeled with time-respecting paths on temporal networks. Here, we propose a framework to quantify memory in time-respecting paths and evaluate it on several empirical datasets encoding proximity between humans collected in different settings. Our results show strong memory effects, robust across settings, model parameters, and statistically significant when compared to memoryless null models. We further propose a generative model to create synthetic temporal graphs with memory and use it to show that memory in time-respecting paths decreases the diffusion speed, affecting the dynamics of spreading processes on temporal networks.