Modelling the scaling properties of human mobility

Ci, Song; Koren, Tal; Wang, Pu; Barabási, Albert László

doi:10.1038/nphys1760

Cited by 1,096 publications

(1,250 citation statements)

References 43 publications

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“…A remarkable recent finding from the analysis of spatiotemporal data on cell-phone locations is that human mobility patterns are highly predictable [2,10,11], a finding that is in contrast to the traditional view. For instance, in epidemic models that take the mobility of subjects into account, subjects are usually assumed to perform a conventional random walk from one location to another [12,13].…”

Section: Introductioncontrasting

confidence: 41%

Predictability of Conversation Partners

et al. 2011

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Recent developments in sensing technologies have enabled us to examine the nature of human social behavior in greater detail. By applying an information-theoretic method to the spatiotemporal data of cellphone locations, [C. Song et al., Science 327, 1018(2010] found that human mobility patterns are remarkably predictable. Inspired by their work, we address a similar predictability question in a different kind of human social activity: conversation events. The predictability in the sequence of one's conversation partners is defined as the degree to which one's next conversation partner can be predicted given the current partner. We quantify this predictability by using the mutual information. We examine the predictability of conversation events for each individual using the longitudinal data of face-to-face interactions collected from two company offices in Japan. Each subject wears a name tag equipped with an infrared sensor node, and conversation events are marked when signals are exchanged between sensor nodes in close proximity. We find that the conversation events are predictable to a certain extent; knowing the current partner decreases the uncertainty about the next partner by 28.4% on average. Much of the predictability is explained by longtailed distributions of interevent intervals. However, a predictability also exists in the data, apart from the contribution of their long-tailed nature. In addition, an individual's predictability is correlated with the position of the individual in the static social network derived from the data. Individuals confined in a community-in the sense of an abundance of surrounding triangles-tend to have low predictability, and those bridging different communities tend to have high predictability.

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Section: Introductioncontrasting

confidence: 41%

Predictability of Conversation Partners

et al. 2011

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“…Finally, we remark that the current method to estimate the user's personalized hybrid parameter is not the best one. For example, analyzing users' historical activity records with time information may lead to a deeper understanding of users' behavior pattern [25] and thus a better prediction of their personalized hybrid parameters. Moreover, users' optimal personalized hybrid parameters will change with time in real systems.…”

Section: Resultsmentioning

confidence: 99%

Preference of online users and personalized recommendations

Yuan

Zhao

Zeng

et al. 2013

Physica A: Statistical Mechanics and its Applications

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In a recent work [Proc. Natl. Acad. Sci. 107 (2010) 4511], a personalized recommendation algorithm with high performance in both accuracy and diversity is proposed. This method is based on the hybridization of two single algorithms called probability spreading and heat conduction, which respectively are inclined to recommend popular and unpopular products. With a tunable parameter, an optimal balance between these two algorithms in system level is obtained. In this paper, we apply this hybrid method in individual level, namely each user has his/her own personalized hybrid parameter to adjust. Interestingly, we find that users are quite different in personalized hybrid parameters and the recommendation performance can be significantly improved if each user is assigned with his/her optimal personalized hybrid parameter. Furthermore, we find that users' personalized parameters are negatively correlated with users' degree but positively correlated with the average degree of the items collected by each user. With these understandings, we propose a strategy to assign users with suitable personalized parameters, which leads to a further improvement of the original hybrid method. Finally, our work highlights the importance of considering the heterogeneity of users in recommendation.

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“…al., 2008;Sevtsuk, 2008) han permitido establecer patrones que implican tanto la presencia de performances persistentes (rutas iteradas, exploración y retornos preferenciales) como de variaciones (o anomalías) que modifican constantemente las formas de las trayectorias habituales (Song et. al., 2010).…”

Section: Conclusión El Estudio De La Duración De Los Contextos De Inunclassified

Comunalidad. Sobre el estudio de la ‘duración’ de los contextos de interacción urbanos Communalty. On temporal magnitud of of interaction situations in urban contexts.

Vidal

2016

Revista De Urbanismo

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ResumenEl objetivo de este trabajo es considerar la noción de 'comunalidad' para el estudio de la duración de las situaciones de interacción en los contextos urbanos. Para ello se revisan el rol de la percepción individual en la composición de los parámetros de acción social; la operación de un ejercicio epistemológico ('objetualización') que permite gestionar individual y socialmente lo percibido y como ello se torna 'común' a los actores mediante la proximidad de percepciones ('comunalidad'). Finalmente se propone considerar que la densidad de la 'comunalidad' en la percepción de los individuos puede ser útil para fijar la duración de los contextos de interacción urbanos (ecologías). Palabras Clave

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Modelling the scaling properties of human mobility

Cited by 1,096 publications

References 43 publications

Predictability of Conversation Partners

Predictability of Conversation Partners

Preference of online users and personalized recommendations

Comunalidad. Sobre el estudio de la ‘duración’ de los contextos de interacción urbanos Communalty. On temporal magnitud of of interaction situations in urban contexts.

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