Temporal patterns behind the strength of persistent ties

TL;DR

This study uses mobile phone data to identify that temporal communication patterns, especially burstiness and consistent rhythm, are key predictors of social tie strength and persistence, surpassing traditional structural and intensity measures.

Contribution

It introduces a predictive model showing that temporal features of communication are more effective than structural or intensity measures in forecasting tie persistence.

Findings

Bursty communication ties are more likely to decay.

Stable ties exhibit a consistent communication rhythm.

Ties halt for over 8 times the previous interval tend to decay.

Abstract

Social networks are made out of strong and weak ties having very different structural and dynamical properties. But, what features of human interaction build a strong tie? Here we approach this question from an practical way by finding what are the properties of social interactions that make ties more persistent and thus stronger to maintain social interactions in the future. Using a large longitudinal mobile phone database we build a predictive model of tie persistence based on intensity, intimacy, structural and temporal patterns of social interaction. While our results confirm that structural (embeddedness) and intensity (number of calls) are correlated with tie persistence, we find that temporal features of communication events are better and more efficient predictors for tie persistence. Specifically, although communication within ties is always bursty we find that ties that are more bursty than the average are more likely to decay, signaling that tie strength is not only reflected in the intensity or topology of the network, but also on how individuals distribute time or attention across their relationships. We also found that stable relationships have and require a constant rhythm and if communication is halted for more than 8 times the previous communication frequency, most likely the tie will decay. Our results not only are important to understand the strength of social relationships but also to unveil the entanglement between the different temporal scales in networks, from microscopic tie burstiness and rhythm to macroscopic network evolution.