A complex network analysis of human mobility

Hossmann, Theus; Spyropoulos, Thrasyvoulos; Legendre, Franck

doi:10.1109/infcomw.2011.5928936

Cited by 79 publications

(56 citation statements)

References 19 publications

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“…In the six communication systems examined in this study, the distributions of the strengths of social relationships d showed power law distributions (Fig. 1), similar to those observed in previous studies (Arnaboldi et al, 2012;Song et al, 2012;Fujihara and Miwa, 2014;Hossmann et al, 2011;Hu et al, 2012). Individuals selected social grooming partners in proportion to the strength of their social relationships, that is, the individuals tended to reinforce their strong social relationships; these power law distributions were generated by the Yule-Simon process (Yule, 1925;Simon, 1955;Newman, 2005) (Fig.…”

Section: Introductionsupporting

confidence: 89%

“…O n the basis of the social brain hypothesis (Byrne and Whiten, 1989;Whiten and Byrne, 1997;Dunbar, 2000;Dunbar, 2003), an explanation of social structures, which are typical for humankind, is expected to offer knowledge about human origins, such as the limitation on the number of social relationships (Dunbar, 2000;Dunbar, 2003;Hill and Dunbar, 2003;Gonçalves et al, 2011;Arnaboldi et al, 2013a) and the skewness of social relationships (Zhou et al, 2005;Hossmann et al, 2011;Arnaboldi et al, 2012;Hu et al, 2012;Song et al, 2012;Arnaboldi et al, 2013b;Fujihara and Miwa, 2014), that is, its distributions following a power law (Hossmann et al, 2011;Arnaboldi et al, 2012;Hu et al, 2012;Pachur et al, 2012;Song et al, 2012;Fujihara and Miwa, 2014). These social structures may be caused by human beings' social grooming strategies (Dunbar, 2004).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, we expect that they employ strategies to distribute the limited time resources to maximize benefits from their social relationships (Brown and Brown, 2006;Miritello et al, 2013b;Saramaki et al, 2014). As a result of such strategies, social relationship strengths (as measured by frequency of social grooming (Roberts and Dunbar, 2011;Arnaboldi et al, 2012;Song et al, 2012;Arnaboldi et al, 2013b;Fujihara and Miwa, 2014;Saramaki et al, 2014) may often show a much skewed distribution (Zhou et al, 2005;Arnaboldi et al, 2013b) (distributions following a power law (Hossmann et al, 2011;Arnaboldi et al, 2012;Hu et al, 2012;Song et al, 2012;Fujihara and Miwa, 2014)). …”

Section: Introductionmentioning

confidence: 99%

“…They are useful for constructing theoretical models of social phenomena. Studies (Arnaboldi et al, 2012;Song et al, 2012;Fujihara and Miwa, 2014;Hossmann et al, 2011;Arnaboldi et al, 2013b;Hu et al, 2012;Takano et al, 2016a;Saramaki et al, 2014;Dunbar, 2016;Takano et al, 2016b) previously cited in this paper have also analysed data from SNSs, mobile phones, social network games, and SMSs.…”

Section: Introductionmentioning

confidence: 99%

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Limitations of time resources in human relationships determine social structures

Takano¹,

Fukuda²

2017

Palgrave Commun

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The number of social relationships that a single human being can possibly be involved in is limited because individuals face time constraints (that is, time costs) in constructing and maintaining social relationships; furthermore, the distribution of the strength of such relationships (as measured by frequency of social interaction) looks significantly skewed (a power law distribution), that is, a few strong relationships and many weak relationships. This skewedness suggests that the costs and benefits of bonding with others depend on the strength of the social relationships: if it involves uniform costs and benefits, the distribution would not be skewed. The bonding is known as social grooming; that is, humans strategically construct their social relationships, and thus, complex human societies should also be strategically constructed. Therefore, it is important to know their strategies for understanding human societies. Previous studies provide evidence of social grooming strategies by examining the evolution and the difference between the various social grooming methods. However, quantitative laws that are important for theoretically understanding human societies are still open to investigation. Social big data is a particularly powerful tool for finding such laws. Therefore, we analyse data from six communication systems (Twitter, a social networking site providing two types of interactions, an avatar chat, a mobile phone and a short message service). We find a simple quantitative law by which social relationships are constrained Nm a (a41); here, N is the number of social relationships and m is a mean of the strength of those relationships. The fact that deep social relationships require higher costs per relationship than shallow relationships is suggested by a41 (if the both is equal then a will be 1), because the effect of m on the constraint increases with m. For exploring why a is greater than 1, we conduct an individual-based simulation where social grooming costs are assumed to increase linearly with the strength of social relationships. Our results indicate that this model fits all data sets; that is, it displays an explanation capacity for the phenomenon. In addition, an analysis of this simulation proves our assumption about the social grooming cost increasing with the strength of social relationships as being true. Moreover, it suggests that its gradient increases the width and shallowness of these relationships. The law and its causes suggest that mankind's evolution of social grooming has enabled changing social structures, and the phenomenon is because of the constraints of the social network generation. These findings will contribute towards an explanation of the evolution of the various social grooming methods of humans and their significantly large social group.

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Section: Introductionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Limitations of time resources in human relationships determine social structures

Takano¹,

Fukuda²

2017

Palgrave Commun

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“…For the detection of some network packets, judge the port and the data segment content directly, the detection efficiency is relatively high. But because the intrusion system is aimed at all packet are detected one by one to determine whether exhibit aggressive behavior, so should follow t he common features of first detection of all attacks and then the individual characteristics of the principle, for example: if the first detection of 1P address [14], once found that does not belong to the detection range, immediately detects the next data package and not continue to test the data packets in the other field. This ensures that the efficiency of the whole detection system, but also improves the real-time alarm.…”

Section: Establishing a Model To Describementioning

confidence: 99%

Design of Complex Network Distributed Computing Information Mining Method

Wang¹,

Zheng²

2015

IJGDC

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Extracting human mobility and social behavior from location‐aware traces

Zignani

Gaito

Rossi

2013

Wireless Communications

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The concepts of location and community are rapidly becoming key points in the design of new communication paradigms and in deploying emerging mobile computing services. The need of reliable and quantitative knowledge and predictions of some relevant information, such as which locations are enjoyed by people in their daily lives and how people aggregate within communities, advocates a realistic mobility model able to describe both the human mobility throughout locations and the human attitude to socialize within communities. Unfortunately, so far, neither the concept of location nor the concept of community has been univocally defined. In this paper, we approach the problem from the most basic of starting points, namely by analyzing the real Global Positioning System datasets of human mobility traces. On this elementary basis, the paper provides a few relevant contributions. We firstly derive a deep understanding of the term “location” and at the same time of the notion of community strictly related to it. Secondly, we merge the two concepts into what we call geo‐community. By proceeding from real spatial data rather than from a priori reasonings, we are able to quantitatively describe geo‐communities and infer the probability distributions of all the features of human behavior. Finally, not to lose social implications, we present the method to derive people sociality from geo‐communities. Copyright © 2012 John Wiley & Sons, Ltd.

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A complex network analysis of human mobility

Cited by 79 publications

References 19 publications

Limitations of time resources in human relationships determine social structures

Limitations of time resources in human relationships determine social structures

Design of Complex Network Distributed Computing Information Mining Method

Extracting human mobility and social behavior from location‐aware traces

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