Time allocation in social networks: correlation between social structure and human communication dynamics

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Giovanna Miritello, Rubén Lara, and Esteban Moro
Chapter in the book ”Temporal Networks”, Springer, 2013. Series: Understanding Complex Systems. Holme, Petter; Saramaki, Jari (Eds.) [pdf]

Abstract
dynnetRecent research has shown the deep impact of the dynamics of human interactions (or temporal social networks) on the spreading of information, opinion formation, etc. In general, the bursty nature of human interactions lowers the interaction between people to the extent that both the speed and reach of information diffusion are diminished. Using a large database of 20 million users of mobile phone calls we show evidence this effect is not homogeneous in the social network but in fact, there is a large correlation between this effect and the social topological structure around a given individual. In particular, we show that social relations of hubs in a network are relatively weaker from the dynamical point than those that are poorer connected in the information diffusion process. Our results show the importance of the temporal patterns of communication when analyzing and modeling dynamical process on social networks.

Are you a social keeper or a social explorer?

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In our last (just accepted) paper “Limited communication capacity unveils strategies for human interaction” [pdf] we have found that we humans have different social strategies when we communicate/interact with people. Specifically, the sociability of a person (the total number of contacts in a time interval) which is usually taken as the connectivity in the social network is actually the result of two different human features:

  • Social capacity: the number of relationships humans can maintain opened and which is limited
  • Social activity: the number of relationships human form and destroy as a consequence of their daily tasks, family, events, etc.

Social capacity and activity are very heterogenous and while most individuals have small capacity and activity, some might have large values for those characteristics. The ratio between these characteristics of human interaction determines the social strategy:

  • Social keepers: these people have a small social activity compared to their social capacity, that is, they interact mostly with the same people in a time interval and form/destroy a small number of ties.
  • Social explorers: the opposite strategy, meaning that most of the interactions of these people are form and destroyed rapidly while keeping a very small number of stable connections.
  • Social balanced: most of the people have a balance social strategy in which the number of form/destroy interactions is proportional to their capacity.

To show how these strategies look like, we have produce the following videos where you can see the tie dynamics around a social explorer and a social keeper (red nodes) for a period of 7 months. Note that if you aggregate the activity of these two people over those 7 months, the will have the same connectivity. But clearly their instantaneous network is very different!

So, what do you think is your social strategy? Are you a social explorer or a social keeper?

Social explorer

Social keeper

Note: these videos are produced using R and the igraph library. Learn how to make them in my post here

La ciencia de la caballería andante

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Happy World Book Day

La Caballería andante (…) es una ciencia, dijo Don Quijote (…) que encierra en sí todas o las más ciencias del mundo (…) el que la profesa ha de ser jurisperito, y saber las leyes de la justicia distributiva y conmutativa (…) ha de ser teólogo, para saber dar razón de la cristiana ley que profesa (…); ha de ser médico, principalmente herbolario, parara conocer (…) las yerbas que tienen virtud de sanar las heridas (…); ha de ser astrólogo, para conocer por las estrellas cuántas horas son pasadas la noche (…); ha de saber las matemáticas, porque a cada paso se le ofrecerá tener necesidad de ellas (…).

Knight-errantry (…) is a science, said Don Quixote (…) that comprehends in itself all or most of the sciences of the world, for he who professes it must be a jurist, and must know the rules of justice, distributive and equitable (…) he must be a theologian, so as to be able to give a clear and distinctive reason for the Christian faith he professes (…); must be a physician, and above all a herbalist, so as (…) to know the herbs that have the property of healing wounds (…); he must be an astronomer, so as to know by the stars how many hours of the night have passed (…) He must know mathematics for at every turn some occasion for them will present itself to him (…)

Miguel de Cervantes Saavedra
El Ingenioso Hidalgo Don Quijote de La Mancha
Second part, Chapter XVIII, Madrid 1615

The predictability of consumer visitation patterns

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Coco Krumme, Alejandro Llorente, Manuel Cebrian, Alex (“Sandy”) Pentland & Esteban Moro, Sci. Rep. 3, 1645; DOI:10.1038/srep01645 (2013). [link]

Abstract
eudataWe consider hundreds of thousands of individual economic transactions to ask: how predictable are consumers in their merchant visitation patterns? Our results suggest that, in the long-run, much of our seemingly elective activity is actually highly predictable. Notwithstanding a wide range of individual preferences, shoppers share regularities in how they visit merchant locations over time. Yet while aggregate behavior is largely predictable, the interleaving of shopping events introduces important stochastic elements at short time scales. These short- and long-scale patterns suggest a theoretical upper bound on predictability, and describe the accuracy of a Markov model in predicting a person’s next location. We incorporate population-level transition probabilities in the predictive models, and find that in many cases these improve accuracy. While our results point to the elusiveness of precise predictions about where a person will go next, they suggest the existence, at large time-scales, of regularities across the population.

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Limited communication capacity unveils strategies for human interaction

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Giovanna Miritello, Rubén Lara, Manuel Cebrián and Esteban Moro
arXiv:1304.1979, preprint (2013) [link]

strategiesAbstract
Social connectivity is the key process that characterizes the structural properties of social networks and in turn processes such as navigation, influence or information diffusion. Since time, attention and cognition are inelastic resources, humans should have a predefined strategy to manage their social interactions over time. However, the limited observational length of existing human interaction datasets, together with the bursty nature of dyadic communications have hampered the observation of tie dynamics in social networks. Here we develop a method for the detection of tie activation/deactivation, and apply it to a large longitudinal, cross-sectional communication dataset (≈19 months, ≈ 20 million people). Contrary to the perception of ever-growing connectivity, we observe that individuals exhibit a finite communication capacity, which limits the number of ties they can maintain active. In particular we find that men have an overall higher communication capacity than women and that this capacity decreases gradually for both sexes over the lifespan of individuals (16-70 years). We are then able to separate communication capacity from communication activity, revealing a diverse range of tie activation patterns, from stable to exploratory. We find that, in simulation, individuals exhibiting exploratory strategies display longer time to receive information spreading in the network those individuals with stable strategies. Our principled method to determine the communication capacity of an individual allows us to quantify how strategies for human interaction shape the dynamical evolution of social networks.