ArticleScientific reports2013
Emergence of scaling in human-interest dynamics.
Article in Scientific reports, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Identifying Important Nodes in Trip Networks and Investigating Their Determinants.Entropy (Basel, Switzerland) · 2023Article
- Non-Markovian recovery makes complex networks more resilient against large-scale failures.Nature communications · 2020Article
- Evidence to support common application switching behaviour on smartphones.Royal Society open science · 2019Article
- Non-Linear Diffusion and Power Law Properties of Heterogeneous Systems: Application to Financial Time Series.Entropy (Basel, Switzerland) · 2018Article
- Universal model of individual and population mobility on diverse spatial scales.Nature communications · 2017Article
- Identifying the perceptive users for online social systems.PloS one · 2017Article
- Article
- Unfolding large-scale online collaborative human dynamics.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Stability of similarity measurements for bipartite networks.Scientific reports · 2016Article
- Emoticon-Based Ambivalent Expression: A Hidden Indicator for Unusual Behaviors in Weibo.PloS one · 2016Article
- Uncovering Spatiotemporal Characteristics of Human Online Behaviors during Extreme Events.PloS one · 2015Article
- Bidirectional selection between two classes in complex social networks.Scientific reports · 2014Article
- Memory effect of the online user preference.Scientific reports · 2014Article
- Influence of reciprocal links in social networks.PloS one · 2014Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human behaviors are often driven by human interests. Despite intense recent efforts in exploring the dynamics of human behaviors, little is known about human-interest dynamics, partly due to the extreme difficulty in accessing the human mind from observations. However, the availability of large-scale data, such as those from e-commerce and smart-phone communications, makes it possible to probe into and quantify the dynamics of human interest. Using three prototypical "Big Data" sets, we investigate the scaling behaviors associated with human-interest dynamics. In particular, from the data sets we uncover fat-tailed (possibly power-law) distributions associated with the three basic quantities: (1) the length of continuous interest, (2) the return time of visiting certain interest, and (3) interest ranking and transition. We argue that there are three basic ingredients underlying human-interest dynamics: preferential return to previously visited interests, inertial effect, and exploration of new interests. We develop a biased random-walk model, incorporating the three ingredients, to account for the observed fat-tailed distributions. Our study represents the first attempt to understand the dynamical processes underlying human interest, which has significant applications in science and engineering, commerce, as well as defense, in terms of specific tasks such as recommendation and human-behavior prediction.
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