Identifying the temporal dynamics of densification and sparsification in human contact networks

TL;DR

This paper introduces a maximum-likelihood method to analyze the dynamic processes of densification and sparsification in human contact networks, revealing how social networks grow and shrink over time.

Contribution

It presents a novel, aggregate-data-based approach to estimate population size and connection probabilities, enhancing understanding of global network dynamics.

Findings

Identifies simultaneous mechanisms of network densification and sparsification.

Provides a privacy-preserving method relying on aggregate data.

Improves understanding of collective social network construction and deconstruction.

Abstract

Temporal social networks of human interactions are preponderant in understanding the fundamental patterns of human behavior. In these networks, interactions occur locally between individuals (i.e., nodes) who connect with each other at different times, culminating into a complex system-wide web that has a dynamic composition. Dynamic behavior in networks occurs not only locally but also at the global level, as systems expand or shrink due either to: changes in the size of node population or variations in the chance of a connection between two nodes. Here, we propose a numerical maximum-likelihood method to estimate population size and the probability of two nodes connecting at any given point in time. An advantage of the method is that it relies only on aggregate quantities, which are easy to access and free from privacy issues. Our approach enables us to identify the simultaneous (rather than the asynchronous) contribution of each mechanism in the densification and sparsification of human contacts, providing a better understanding of how humans collectively construct and deconstruct social networks.