Entangling mobility and interactions in social media

TL;DR

This paper introduces a new model that couples social interactions and mobility, demonstrating how physical location influences social network formation and vice versa, validated with data from multiple online social platforms.

Contribution

It presents a novel model explicitly linking mobility and social ties, filling a gap in existing research by capturing their mutual influence and reproducing real-world network properties.

Findings

Model reproduces topological properties of social networks

Distance influences reciprocity and clustering

Balance of local visits and long-range connections is crucial

Abstract

Daily interactions naturally define social circles. Individuals tend to be friends with the people they spend time with and they choose to spend time with their friends, inextricably entangling physical location and social relationships. As a result, it is possible to predict not only someone's location from their friends' locations but also friendship from spatial and temporal co-occurrence. While several models have been developed to separately describe mobility and the evolution of social networks, there is a lack of studies coupling social interactions and mobility. In this work, we introduce a new model that bridges this gap by explicitly considering the feedback of mobility on the formation of social ties. Data coming from three online social networks (Twitter, Gowalla and Brightkite) is used for validation. Our model reproduces various topological and physical properties of these networks such as: i) the size of the connected components, ii) the distance distribution between connected users, iii) the dependence of the reciprocity on the distance, iv) the variation of the social overlap and the clustering with the distance. Besides numerical simulations, a mean-field approach is also used to study analytically the main statistical features of the networks generated by the model. The robustness of the results to changes in the model parameters is explored, finding that a balance between friend visits and long-range random connections is essential to reproduce the geographical features of the empirical networks.