Time as a limited resource: Communication Strategy in Mobile Phone Networks

We used a large database of 9 billion calls from 20 million mobile users to examine the relationships between aggregated time spent on the phone, personal network size, tie strength and the way in which users distributed their limited time across their network (disparity). Compared to those with smaller networks, those with large networks did not devote proportionally more time to communication and had on average weaker ties (as measured by time spent communicating). Further, there were not substantially different levels of disparity between individuals, in that mobile users tend to distribute their time very unevenly across their network, with a large proportion of calls going to a small number of individuals. Together, these results suggest that there are time constraints which limit tie strength in large personal networks, and that even high levels of mobile communication do not fundamentally alter the disparity of time allocation across networks.Comment: 10 pages, 3 figures. Accepted for publication in Social Network

Influence of Personal Preferences on Link Dynamics in Social Networks

We study a unique network dataset including periodic surveys and electronic logs of dyadic contacts via smartphones. The participants were a sample of freshmen entering university in the Fall 2011. Their opinions on a variety of political and social issues and lists of activities on campus were regularly recorded at the beginning and end of each semester for the first three years of study. We identify a behavioral network defined by call and text data, and a cognitive network based on friendship nominations in ego-network surveys. Both networks are limited to study participants. Since a wide range of attributes on each node were collected in self-reports, we refer to these networks as attribute-rich networks. We study whether student preferences for certain attributes of friends can predict formation and dissolution of edges in both networks. We introduce a method for computing student preferences for different attributes which we use to predict link formation and dissolution. We then rank these attributes according to their importance for making predictions. We find that personal preferences, in particular political views, and preferences for common activities help predict link formation and dissolution in both the behavioral and cognitive networks.Comment: 12 page

Events in social networks : a stochastic actor-oriented framework for dynamic event processes in social networks

Interactions between people are ubiquitous. When people make phone calls, transfer money, connect on social network sites, or visit each other, these actions can be collected as dyadic, directed, relational events. Each of those events can be understood as driven by multiple individual decisions that at least partially involve rational considerations. This book aims at developing models that allow to understand individual event decisions in the context of large social networks

Temporal patterns behind the strength of persistent ties

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 a 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) features are correlated with tie persistence, 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.EM acknowledges funding from Ministerio de Economía y Competividad (Spain) through projects FIS2013-47532-C3-3-P and FIS2016-78904-C3-3-P

What comes first? Social strength or common friends?

[Abstract of]: NetMob, 5-7 April 2017, Milan, ItalySocial networks are made out of strong andweak links having very different structural anddynamical properties. Social scientists have longrecognized the value of weak links in information discovery but also their relative structuralweakness that makes them more likely to decayin the future. What what features of humaninteraction build a strong tie? Here we approachthis question from an practical way by findingwhat are the properties of social interactions thatmake ties more persistent and thus stronger tomaintain social interactions in the future. Usinga large longitudinal mobile phone database webuild a predictive model of tie persistence basedon intensity, structural and temporal patternsof social interaction. While our results confirmthat structural (embeddedness) and intensity(number of calls) are correlated with tie persistence, we find that daily temporal featuresof communication events in a tie are betterand more efficient predictors for tie persistence.Specifically, although communication betweenlinks is always bursty we find that links that aremore bursty than the average are more likely todecay, signaling that the strength of the tie isnot only reflected in the intensity or topology ofthe network, but also on how we distribute intime our interactions with our relationships. Ourresults not only are important to understandthe strength of social relationships but also tounveil the entanglement between the differenttemporal scales in networks, from microscopictie burstiness to network evolution