Temporal patterns of emergency calls of a metropolitan city in China

Wang, Wenjun; Yuan, Ning; Pan, Lin; Jiao, Pengfei; Dai, Weihui; Xue, Guixiang

doi:10.1016/j.physa.2015.05.028

Cited by 26 publications

(33 citation statements)

References 35 publications

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“…Based on these empirical observations, one might conclude that most of human activities do not show strong correlations between IETs. On the other hand, the empirical value of β for the burst size distributions varies from 2.5 for earthquakes in Japan to 2.8-3.0 for Wikipedia editing patterns [35] and 3.9-4.2 for mobile phone communication patterns [7,34], while it is found that β ≈ 2.21 in the emergency call dataset [31]. Since the power-law behaviors of burst size distributions for a wide range of time windows imply the complex, higher-order correlations between IETs, this seems to be inconsistent with the weak correlation implied by the observation M ≈ 0 in human activities.…”

Section: Limits Of the Memory Coefficient In Measuring Correlationsmentioning

confidence: 95%

“…for a wide range of ∆t, e.g., in earthquakes, neuronal activities, and human communication patterns [7,31,34,35]. This indicates the presence of higher-order correlations between IETs beyond the correlations measured by M [36].…”

Section: A Measures and Characterizationsmentioning

confidence: 99%

“…Negative M implies the opposite tendency, while M = 0 is for the uncorrelated IETs. In many empirical analyses, positive M has been observed [17,[31][32][33].…”

Section: A Measures and Characterizationsmentioning

confidence: 99%

“…The memory coefficient, measuring the correlations only between two consecutive IETs, has been used to analyze event sequences in natural phenomena and human activities as well as to test models for bursty dynamics [17,31,33,38]. It has been found that M ≈ 0.2 for earthquakes in Japan, while M is close to 0 or less than 0.1 for various human activities [17].…”

Section: Limits Of the Memory Coefficient In Measuring Correlationsmentioning

confidence: 99%

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Bursty Time Series Analysis for Temporal Networks

Hiraoka

2019

Computational Social Sciences

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Characterizing bursty temporal interaction patterns of temporal networks is crucial to investigate the evolution of temporal networks as well as various collective dynamics taking place in them. The temporal interaction patterns have been described by a series of interaction events or event sequences, often showing non-Poissonian or bursty nature. Such bursty event sequences can be understood not only by heterogeneous interevent times (IETs) but also by correlations between IETs. The heterogeneities of IETs have been extensively studied in recent years, while the correlations between IETs are far from being fully explored. In this Chapter, we introduce various measures for bursty time series analysis, such as the IET distribution, the burstiness parameter, the memory coefficient, the bursty train sizes, and the autocorrelation function, to discuss the relation between those measures. Then we show that the correlations between IETs can affect the speed of spreading taking place in temporal networks. Finally, we discuss possible research topics regarding bursty time series analysis for temporal networks. * This is a preprint for a chapter to appear in Temporal Network Theory edited by P. Holme and J. Saramäki (https: //www.springer.com/gp/book/9783030234942).

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Section: Limits Of the Memory Coefficient In Measuring Correlationsmentioning

confidence: 95%

Section: A Measures and Characterizationsmentioning

confidence: 99%

“…Negative M implies the opposite tendency, while M = 0 is for the uncorrelated IETs. In many empirical analyses, positive M has been observed [17,[31][32][33].…”

Section: A Measures and Characterizationsmentioning

confidence: 99%

Section: Limits Of the Memory Coefficient In Measuring Correlationsmentioning

confidence: 99%

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Bursty Time Series Analysis for Temporal Networks

Hiraoka

2019

Computational Social Sciences

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“…A lot of researchers have found that such as web browsing [1], [5], blog comments [6], market trading [7], human communication behavior [8]- [10], logistics transportation [11] and other human behavior all obey the power law relationship .Therefore, many researchers studied the method to determine whether a distribution is a power law distribution and an estimate of the power exponent.…”

Section: Methodsmentioning

confidence: 99%

An Empirical Research of Human Behavior Dynamics in Network Course Learning

Cheng¹,

Yan²

2017

IJIET

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Abstract-Learning is a kind of important human behavior to acquire knowledge. This paper discussed temporal characteristics of network course learning on behavior dynamics. Firstly, the students behavior data of the network course learning for 8-weeks are collected from online learning platform. Then, the work used the Maximum Likelihood Estimation(MLE) method for estimating the power exponent of learning behavior interval time distribution, and introduced Kolmogorov-Smirnov(KS) method to test power-law hypothesis .The empirical research results show that: both in the group and individual level, learning behavior time interval obey characteristics of power-law distribution. Underlying these, This thesis combined with the learning psychology, environment and other factors explained the statistical characteristics, and provided some suggestions for teaching management.Index Terms-Empirical research, behavior dynamics, behavior interval time distribution, power-law.

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Time to vote: Temporal clustering of user activity on Stack Overflow

Geras

Siudem

Gągolewski

2022

Asso for Info Science & Tech

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Question‐and‐answer (Q&A) sites improve access to information and ease transfer of knowledge. In recent years, they have grown in popularity and importance, enabling research on behavioral patterns of their users. We study the dynamics related to the casting of 7 M votes across a sample of 700 k posts on Stack Overflow, a large community of professional software developers. We employ log‐Gaussian mixture modeling and Markov chains to formulate a simple yet elegant description of the considered phenomena. We indicate that the interevent times can naturally be clustered into 3 typical time scales: those which occur within hours, weeks, and months and show how the events become rarer and rarer as time passes. It turns out that the posts' popularity in a short period after publication is a weak predictor of its overall success, contrary to what was observed, for example, in case of YouTube clips. Nonetheless, the sleeping beauties sometimes awake and can receive bursts of votes following each other relatively quickly.

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Temporal patterns of emergency calls of a metropolitan city in China

Cited by 26 publications

References 35 publications

Bursty Time Series Analysis for Temporal Networks

Bursty Time Series Analysis for Temporal Networks

An Empirical Research of Human Behavior Dynamics in Network Course Learning

Time to vote: Temporal clustering of user activity on Stack Overflow

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