We investigate the temporal patterns of human communication and its influence on the spreading of information in social networks. The analysis of mobile phone calls of 20 million people in one country shows that human communication is bursty and happens in group conversations. These features have opposite effects in information reach: while bursts hinder propagation at large scales, conversations favor local rapid cascades. To explain these phenomena we define the dynamical strength of social ties, a quantity that encompasses both the topological and temporal patterns of human communication.Quantitative understanding of human communication patterns is of paramount importance to explain the dynamics of many social, technological and economic phenomena [1][2][3][4]. Most studies have focused on the study of the complex topological patterns of the underlying contact network (whom we talk to) and its influence in the properties of spreading phenomena in social networks such diffusion of information, innovations, computer viruses, opinions, etc. [2]. Paradoxically, most of these studies of dynamical phenomena on social networks neglect the temporal patterns of human communications: humans act in bursts or cascades of events [5][6][7][8], most ties are not persistent [9,10] and communications happen mostly in the form of group conversations [8,[11][12][13]. However, since information transmission and human communication are concurrent, the temporal structure of communication must influence the properties of information spreading. Indeed, recent experiments of electronic recommendation forwarding [14] and simulations of epidemic models on email and mobile databases [6,15] found that the asymptotic speed of information spreading is controlled by the bursty nature of human communications that leads to a slowing down of the diffusion. However, although the asymptotic speed is an important property of the propagation of information in social networks, there is still no general understanding of how and what temporal properties of human communication do influence spreading processes and in turn, how they affect the very definition of social interaction.The answer to this question can be framed in the more general problem of how to model dynamical social networks [9,16]. In most studies, real temporal activity is aggregated over time giving a static snapshot of the social interaction where ties are described by static strengths which do not include information about the temporal aspects of how humans interact. Temporal and topological aspects are therefore disentangled in the analysis. In this letter we merge both aspects in the case of information diffusion by adopting a functional definition of the social ties using the well-known map between dynamical epidemic models and static percolation [17]. The network is still described by a static graph, but the interaction strength between individuals now incorporates the causal and temporal patterns of their communications and not only on the intensity [18].To this end we study th...
Organic matter, which is dissolved in low concentrations in the vast waters of the oceans, contains a total amount of carbon similar to atmospheric carbon dioxide. To understand global biogeochemical cycles, it is crucial to quantify the sources of marine dissolved organic carbon (DOC). We investigated the impact of mangroves, the dominant intertidal vegetation of the tropics, on marine DOC inventories. Stable carbon isotopes and proton nuclear magnetic resonance spectroscopy showed that mangroves are the main source of terrigenous DOC in the open ocean off northern Brazil. Sunlight efficiently destroyed aromatic molecules during transport offshore, removing about one third of mangrove‐derived DOC. The remainder was refractory and may thus be distributed over the oceans. On a global scale, we estimate that mangroves account for >10% of the terrestrially derived, refractory DOC transported to the ocean, while they cover only <0.1% of the continents' surface.
Connectivity is the key process that characterizes the structural and functional properties of social networks. However, the bursty activity of dyadic interactions may hinder the discrimination of inactive ties from large interevent times in active ones. We develop a principled method to detect tie de-activation 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 time. On average men display higher capacity than women, and this capacity decreases for both genders over their lifespan. Separating communication capacity from activity reveals a diverse range of tie activation strategies, from stable to exploratory. This allows us to draw novel relationships between individual strategies for human interaction and the evolution of social networks at global scale.
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. I. INTRODUCTIONDuring the last two decades the structural and the dynamic properties of social networks have been subject of intensive study (Watts,55). The structure of social networks is important not only from the perspective of the single user, but also from that of society as a whole, as it can influence various dynamic processes of human interaction, communication, spreading of information and disease transmission (Christakis and Fowler,11, Onnela et al.,38, Watts,55). Traditionally, these communication networks have been studied on a relatively small scale, using questionnaire or interview methods to gather data on how communication patterns are related to other characteristics such as social support (Wellman and Frank, 59) or the emotional intensity of the tie between two individuals (Roberts and Dunbar, 49). However, people's recollection of specific communication events is often imperfect (Bernard et al, 7) and the extent to which studies on specific, limited samples can be generalized to wider populations and countries is unclear (Henrich et al.,24, Wellman,57). With the rise of electronically-mediated communication, it is now becoming possible to study communication patterns in networks on a scale, and at a level of detail, not possible using traditional questionnaire or survey methods (Bohannon,10, Lazer et al.,29, Watts,56). Specifically in terms of mobile phone communication, access to data on this scale has led to advances in our understanding about the structure of mobile phone networks, network dynamics, factors influencing information transmission in the networks and reciprocity of communication (Miritello et al.,34, Onnela et al.,39, Palla et al.,42).One key variable that characterizes the structural topology of such networks is the social connectivity or degree of a node. It measures the number of people with whom an individual interacts and can also be interpreted as a measure of social integration (Marsden, 32) or activity (Wasserman et al.,54). In general, the degree distributions are skewed with a long tail, indicating that most users have on...
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