Modeling memory in time-respecting paths on temporal networks
This work provides a method to measure and model memory in temporal networks, which is important for understanding and predicting spreading processes like disease transmission.
The authors propose a framework to quantify memory in time-respecting paths on temporal networks, finding strong memory effects across multiple human proximity datasets. They show that memory decreases diffusion speed in spreading processes.
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.