Empirical Analysis on the Human Dynamics of a Large-Scale Short Message Communication System

Zhao, Zhi-Dan; Hu, X. Joan; Shang, Mingsheng; Zhou, Tao

doi:10.1088/0256-307x/28/6/068901

Cited by 56 publications

(25 citation statements)

References 35 publications

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“…Such data often come either in the form of lists of messages from one person to another at a point in time, or a dialogue between two persons within a time interval. The first type contains networks of e-mail messages [39,61,120,157], mobile phone text messages [162,169], and instant messages and messages in online forums [58,95]. Phone calls are not instantaneous but have a specific duration, and can thus be considered to be of the second type [21,71,107,116,120].…”

Section: A Person-to-person Communicationmentioning

confidence: 99%

Temporal networks

2012

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A great variety of systems in nature, society and technology-from the web of sexual contacts to the Internet, from the nervous system to power grids-can be modeled as graphs of vertices coupled by edges. The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems. In many cases, however, the edges are not continuously active. As an example, in networks of communication via email, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts. In some cases, edges are active for non-negligible periods of time: e.g., the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward. Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network. In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems. In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself. Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks. The study of temporal networks is very interdisciplinary in nature. Reflecting this, even the object of study has many names

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Section: A Person-to-person Communicationmentioning

confidence: 99%

Temporal networks

2012

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“…Examples include the email communication [22], the cell-phone communication [23], the short-message communication [24,25,26], the web page visits [27,28,29] and some other online activities [30,31]. To name just a few.…”

Section: Introductionmentioning

confidence: 99%

Impact of heterogeneous human activities on epidemic spreading

Yang

Cui

Zhou

2011

Physica A: Statistical Mechanics and its Applications

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Recent empirical observations suggest a heterogeneous nature of human activities. The heavy-tailed inter-event time distribution at population level is well accepted, while whether the individual acts in a heterogeneous way is still under debate. Motivated by the impact of temporal heterogeneity of human activities on epidemic spreading, this paper studies the susceptible-infected model on a fully mixed population, where each individual acts in a completely homogeneous way but different individuals have different mean activities. Extensive simulations show that the heterogeneity of activities at population level remarkably affects the speed of spreading, even though each individual behaves regularly. Further more, the spreading speed of this model is more sensitive to the change of system heterogeneity compared with the model consisted of individuals acting with heavy-tailed inter-event time distribution. This work refines our understanding of the impact of heterogeneous human activities on epidemic spreading.

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“…They are characterized by a cascading process with branching rate a < 1, namely, each event has a chance a to induce a responsive event. Inspired by the known empirical observations (2,3,6,13,(30)(31)(32)(33), the interval time between an event and its possibly induced event (i.e., waiting time) follows a power-law distribution as follows:…”

Section: Modelmentioning

confidence: 99%

Unfolding large-scale online collaborative human dynamics

Zha

Zhou

2016

Proc. Natl. Acad. Sci. U.S.A.

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Large-scale interacting human activities underlie all social and economic phenomena, but quantitative understanding of regular patterns and mechanism is very challenging and still rare. Self-organized online collaborative activities with a precise record of event timing provide unprecedented opportunity. Our empirical analysis of the history of millions of updates in Wikipedia shows a universal double-power-law distribution of time intervals between consecutive updates of an article. We then propose a generic model to unfold collaborative human activities into three modules: (i) individual behavior characterized by Poissonian initiation of an action, (ii) human interaction captured by a cascading response to previous actions with a power-law waiting time, and (iii) population growth due to the increasing number of interacting individuals. This unfolding allows us to obtain an analytical formula that is fully supported by the universal patterns in empirical data. Our modeling approaches reveal "simplicity" beyond complex interacting human activities.human dynamics | online collaboration | double power law | multibranching Q uantitative understanding of regular patterns in human dynamics is of great importance but fairly challenging because they are driven by complex decision-making processes, involving competing choices under limited time and cost, interactions with social peers, influences from the external environment, and so on. Previously, when detailed and precise records of human activities were rare, individual activities were assumed to follow random Poissonian processes with exponential distributions of interevent times (1). In contrast, recent analysis and experiments on deliberate human behaviors, such as communication through emails (2), surface mails (3), cell phones (4), instant messages (5), text messages (6), social contacts (7), and online activities including web browsing (8), movie watching (9), searching (10), and shopping (11), showed that individual activities usually embody the bursty nature featured by fat-tailed, power-law-like distributions of interevent times. Several models have been proposed to explain the possible underlying mechanisms, including a task competition model driven by individual decision (2, 12, 13), a nonstationary Poisson process driven by daily and weekly circadian circles (14-16), and adaptive interest (17). Besides the complexity in individual decision making, these works do not take the interaction between individuals into explicit consideration. The interaction was theoretically explored by placing a task competition model into a network of two (18) or more agents (19)(20)(21)(22). Only recently, it was explicitly demonstrated that interaction can lead to a bimodal distribution of the interevent times in short message communication, which is mainly a two-person interaction system (23). Thus far, how interactions happen in a real system organized by a large number of individuals is still unknown.Modern information technology has allowed many new types of human inte...

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Empirical Analysis on the Human Dynamics of a Large-Scale Short Message Communication System

Cited by 56 publications

References 35 publications

Temporal networks

Temporal networks

Impact of heterogeneous human activities on epidemic spreading

Unfolding large-scale online collaborative human dynamics

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