Understanding mobility in a social petri dish

Szell, Michael; Sinatra, Roberta; Petri, Giovanni; Thurner, Stefan; Latora, Vito

doi:10.1038/srep00457

Cited by 113 publications

(133 citation statements)

References 44 publications

(85 reference statements)

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“…resembling provinces) as well as clusters in the social network consisting of groups of well connected people [21][22][23], although the connections between the two are not yet fully understood [24]. Similar results have also been obtained in a virtual mobility setting [25].…”

Section: Introductionsupporting

confidence: 73%

Exploring the mobility of mobile phone users

Csáji

Browet

Traag

et al. 2013

Physica A: Statistical Mechanics and its Applications

191

143

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Mobile phone datasets allow for the analysis of human behavior on an unprecedented scale. The social network, temporal dynamics and mobile behavior of mobile phone users have often been analyzed independently from each other using mobile phone datasets. In this article, we explore the connections between various features of human behavior extracted from a large mobile phone dataset. Our observations are based on the analysis of communication data of 100000 anonymized and randomly chosen individuals in a dataset of communications in Portugal. We show that clustering and principal component analysis allow for a significant dimension reduction with limited loss of information. The most important features are related to geographical location. In particular, we observe that most people spend most of their time at only a few locations. With the help of clustering methods, we then robustly identify home and office locations and compare the results with official census data. Finally, we analyze the geographic spread of users' frequent locations and show that commuting distances can be reasonably well explained by a gravity model. Keywords:Human Mobility, Data Mining, Location Detection, Commuting Distance AbstractMobile phone datasets allow for the analysis of human behavior on an unprecedented scale. The social network, temporal dynamics and mobile behavior of mobile phone users have often been analyzed independently from each other using mobile phone datasets. In this article, we explore the connections between various features of human behavior extracted from a large mobile phone dataset. We show that clustering and principal component analysis allows for a significant dimension reduction with limited loss of information. The most important features are related to geographical location. In particular, we observe that most people spend most of their time at only a few locations. of users' frequent locations and show that commuting distances can be reasonably well explained by a gravity model.

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Section: Introductionsupporting

confidence: 73%

Exploring the mobility of mobile phone users

Csáji

Browet

Traag

et al. 2013

Physica A: Statistical Mechanics and its Applications

191

143

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“…The visiting behaviors for different values of λ (varied from 0 to 0.6) are realized 10 3 times and the values of σ and f are plotted (closed symbols). We (somewhat arbitrarily) set the model parameters to be S = 100 and ρ = 0.6, as in previous works [3,12,24]. The maximum number of visit is n max = 1 × 10 5 , at which time the exploration probability p new approaches zero.…”

mentioning

confidence: 99%

“…3. To initiate a trajectory either in cyberspace or in physical space, an individual has two options [3,24]: to explore a new site with probability p new or to return to a previously visited site with probability 1 − p new . Based on a combination of numerical calculation and physical reasoning, we show below that the model successfully predicts the superlinear scaling relation between σ and f .…”

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confidence: 99%

Scaling and correlation of human movements in cyberspace and physical space

Zhao

Huang

et al. 2014

Phys. Rev. E

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Understanding the dynamics of human movements is key to issues of significant current interest such as behavioral prediction, recommendation, and control of epidemic spreading. We collect and analyze big data sets of human movements in both cyberspace (through browsing of websites) and physical space (through mobile towers) and find a superlinear scaling relation between the mean frequency of visit f and its fluctuation σ : σ ∼ f β with β ≈ 1.2. The probability distribution of the visiting frequency is found to be a stretched exponential function. We develop a model incorporating two essential ingredients, preferential return and exploration, and show that these are necessary for generating the scaling relation extracted from real data. A striking finding is that human movements in cyberspace and physical space are strongly correlated, indicating a distinctive behavioral identifying characteristic and implying that the behaviors in one space can be used to predict those in the other. Traditionally, human movements are restricted to the real physical space (or geospace). Pioneering works demonstrated that there are intrinsic patterns underlying human mobility in physical space [1][2][3], which are key to deciphering the dynamics of human behaviors with wide applications ranging from traffic forecasting [4] to epidemic prevention [5]. Triggered by the tremendous advances in modern information and communication technologies, at present as well as in the future, human movements occur not only in physical space but also in virtual or cyberspace. Here movements in cyberspace are defined broadly as changes in online activities, typically corresponding to switchings in the websites of exploration. Examples of cyberspace movements include World Wide Web surfing along hyperlinks and continuous shopping from commercial websites in a single online session. Do human movements in cyberspace and physical space share common features? Are there general scaling relations underlying human movements in both spaces?Studies of human behaviors have been greatly facilitated by the ubiquity of massive empirical data sets (big data sets) that typically record individuals' movements on various temporal and spatial scales [6,7]. For example, great insights into the dynamics of human movements in physical space were gained by tracking and analyzing the dispersal of dollar bills [1] and through mobile phone [2] and GPS [8] data. There were also efforts to uncover human movements in cyberspace during web surfing [9][10][11] and to probe into human interests dynamics unfolded during cyberspace shopping and browsing [12].In this paper we analyze data sets that record mobile phone users' visits to websites in cyberspace and to mobile towers in the physical space simultaneously and search for a correlation between the movements and general scaling relations. Distinguished from existing approaches to humanmobility analysis [1-3], we focus on the relationship between * ying-cheng.lai@asu.edu flux and fluctuations [13][14][15][16][17][18][19]. In pa...

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“…In the game Pardus [15], data acquisition makes it feasible to study social theories in large-scale virtual-world populations [16][17][18][19][20][21][22]. Various approaches to analyzing the structure and dynamic evolution of social networks in virtual worlds have been developed and have yielded significant findings [23,24].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Currency Market in Online Gaming via Lexicon-Based Analysis on Its Online Forum

Kim¹,

Kang²,

Choo³

et al. 2017

Complexity

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Transactions involving virtual currencies are becoming increasingly common, including those in online games. In response, predicting the market price of a virtual currency is an important task for all involved, but it has not yet attracted much attention from researchers. This paper presents user opinions from online forums in a massive multiplayer online game (MMOG) setting widely used around the world. We propose a method for predicting the next-day rise and fall of the currency used in an MMOG environment. Based on analysis of online forum users' opinions, we predict daily fluctuations in the price of a currency used in an MMOG setting. Focusing specifically on the World of Warcraft game, one of the most widely used MMOGs, we demonstrate the feasibility of predicting the fluctuation in value of virtual currencies used in this game community.

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Understanding mobility in a social petri dish

Cited by 113 publications

References 44 publications

Exploring the mobility of mobile phone users

Exploring the mobility of mobile phone users

Scaling and correlation of human movements in cyberspace and physical space

Predicting the Currency Market in Online Gaming via Lexicon-Based Analysis on Its Online Forum

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