The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Evidence for a bimodal distribution in human communication
Open accessAbstract
Interacting human activities underlie the patterns of many social, technological, and economic phenomena. Here we present clear empirical evidence from Short Message correspondence that observed human actions are the result of the interplay of three basic ingredients: Poisson initiation of tasks and decision making for task execution in individual humans as well as interaction among individuals. This interplay leads to new types of interevent time distribution, neither completely Poisson nor power-law, but a bimodal combination of them. We show that the events can be separated into independent bursts which are generated by frequent mutual interactions in short times following random initiations of communications in longer times by the individuals. We introduce a minimal model of two interacting priority queues incorporating the three basic ingredients which fits well the distributions using the parameters extracted from the empirical data. The model can also embrace a range of realistic social interacting systems such as e-mail and letter communications when taking the time scale of processing into account. Our findings provide insight into various human activities both at the individual and network level. Our analysis and modeling of bimodal activity in human communication from the viewpoint of the interplay between processes of different time scales is likely to shed light on bimodal phenomena in other complex systems, such as interevent times in earthquakes, rainfall, forest fire, and economic systems, etc.
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Cites
Statistical mechanics of complex networks
Albert, R., & Barabási, A.-L. (2002) · Reviews of Modern Physics · doi:10.1103/revmodphys.74.47 · Cited by 20,471
The Structure and Function of Complex Networks
Newman, M. E. J. (2003) · SIAM Review · doi:10.1137/s003614450342480 · Cited by 18,719
Complex networks: structure and dynamics
BOCCALETTI, S., LATORA, V, MORENO, Y., CHAVEZ, M., & HWANG, D. (2006) · Physics Reports · doi:10.1016/j.physrep.2005.10.009 · Cited by 11,010
The origin of bursts and heavy tails in human dynamics
Barabási, A.-L. (2005) · Nature · doi:10.1038/nature03459 · Cited by 2,210
Solving the apparent diversity-accuracy dilemma of recommender systems
Zhou, T., Kuscsik, Z., Liu, J.-G., Medo, M., Wakeling, J. R., & Zhang, Y.-C. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1000488107 · Cited by 972
Modeling bursts and heavy tails in human dynamics
Vázquez, A., Oliveira, J. G., Dezsö, Z., Goh, K.-I., Kondor, I., & Barabási, A.-L. (2006) · Physical Review E · doi:10.1103/physreve.73.036127 · Cited by 673
Handbook of the Poisson Distribution.
Bassett, E. E., & Haight, F. A. (1968) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2343850 · Cited by 546
Uncovering individual and collective human dynamics from mobile phone records
Candia, J., González, M. C., Wang, P., Schoenharl, T., Madey, G., & Barabási, A.-L. (2008) · Journal of Physics A: Mathematical and Theoretical · doi:10.1088/1751-8113/41/22/224015 · Cited by 477
Entropy of dialogues creates coherent structures in e-mail traffic
Eckmann, J.-P., Moses, E., & Sergi, D. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0405728101 · Cited by 444
Impact of Human Activity Patterns on the Dynamics of Information Diffusion
Iribarren, J. L., & Moro, E. (2009) · Physical Review Letters · doi:10.1103/physrevlett.103.038702 · Cited by 413
Cited by
Holme, P., & Saramäki, J. (2012) · Physics Reports · doi:10.1016/j.physrep.2012.03.001 · Cited by 2,736
“The first type contains networks of e-mail messages [11,17–19], mobile phone text messages [20,21], and instant messages and messages in online forums [22,23].”
BackgroundA literature survey on smart cities
Yin, C., Xiong, Z., Chen, H., Wang, J., Cooper, D., & David, B. (2015) · Science China Information Sciences · doi:10.1007/s11432-015-5397-4 · Cited by 734
Robust detection of dynamic community structure in networks
Bassett, D. S., Porter, M. A., Wymbs, N. F., Grafton, S. T., Carlson, J. M., & Mucha, P. J. (2013) · Chaos: An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4790830 · Cited by 425
Universal features of correlated bursty behaviour
Karsai, M., Kaski, K., Barabási, A.-L., & Kertész, J. (2012) · Scientific Reports · doi:10.1038/srep00397 · Cited by 310
Dynamical strength of social ties in information spreading
Miritello, G., Moro, E., & Lara, R. (2011) · Physical Review E · doi:10.1103/physreve.83.045102 · Cited by 306
Metadata
- Authors
- Wu, Y., Zhou, C., Xiao, J., Kurths, J., & Schellnhuber, H. J.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2010-10-19
- Volume
- 107
- Issue
- 44
- Pages
- 18803-18808
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W1972019869
- Cited by
- 269
- References
- 29
- FWCI
- 54.54
- Type
- journal article
- Language
- en
- Open access
- green
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- Wu, Y., Zhou, C., Xiao, J., Kurths, J., & Schellnhuber, H. J. (2010). Evidence for a bimodal distribution in human communication. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1013140107
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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