Scale-free topology of e-mail networks

Ebel, Holger; Mielsch, Lutz-Ingo; Bornholdt, Stefan

doi:10.1103/physreve.66.035103

Cited by 804 publications

(526 citation statements)

References 24 publications

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“…The number of distinct network structures possible with just 16 nodes is 6.4 × 10 22 . To complicate matters, human networks-both organic and designed-often exhibit peculiar properties such as a scale-free degree distribution (see, for example, Ebel et al, 2002), and at large scales may have partitioning characteristics that do not match any of the common models for random network generation (Leskovec et al, 2009). Thus there are no clear criteria for generating the most representative or relevant sample of networks from the vast variety of possible networks.…”

Section: Experimental Tests Of the Effect Of Network Knowledge On Coomentioning

confidence: 99%

Knowledge and networks: An experimental test of how network knowledge affects coordination

2014

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a b s t r a c tScholars in the social sciences use network theory to study a range of collective action problems. Often the goal is to identify how the structure of the network affects efforts to coordinate or cooperate, and research suggests that adding connections to a network can improve the performance of groups faced with such tasks. On the other hand, theory and empirics also suggest that additional connections can degrade the performance of a network. If connections can have negative effects then it is important to consider if there are alternatives to adding connections to a network that can also improve network performance. Because a primary function of connections in a network is to disseminate information, providing individuals with more information about the network may act as a substitute for adding connections to a network. We test experimentally whether providing subjects with more information about the structure of networks can improve coordination. We find that a more complete view of the network leads to faster coordination, but the magnitude of this effect depends on network structure. These results suggest that changing what actors know about a network can improve outcomes without having to add connections that may impede overall performance.

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Section: Experimental Tests Of the Effect Of Network Knowledge On Coomentioning

confidence: 99%

Knowledge and networks: An experimental test of how network knowledge affects coordination

2014

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“…During the last decade several groups have studied the structure of social networks as expressed in e-mails [ 1,2 ], cellular phones [ 3,4 ] and work relationships such as starring on a movie [ 5 ] or collaborating in a paper [ 6 ]. Studying the dynamics of such social systems however, has been limited by the lack of longitudinal data and as a result only a few studies on the dynamics of interpersonal connections have been produced [ 1,3,7 ].…”

Section: Introductionmentioning

confidence: 99%

“…Automatically collected communication records have been proposed as a source of reliable data about personal connections [ 13 ]. Email data has been used to study social processes such as tie formation [ 1 ] and social structure [ 2 ]. The citations patterns of web logs have been used to study the spread of political opinions [ 14 ] and the growth of an online dating community has been documented [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

The dynamics of a mobile phone network

Hidalgo

Rodríguez-Sickert

2008

Physica A: Statistical Mechanics and its Applications

165

133

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“…Because all of the outgoing and incoming contacts were recorded for internal nodes, their in and out degrees were higher than for the external nodes for which we could only record the email they sent to and received from HP Labs. We however considered a graph with the internal and external nodes mixed (as in [18]) to demonstrate the effect of a decay on the spread of email specifically in a power-law graph.…”

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confidence: 99%

Information flow in social groups

Huberman

Adamic

et al. 2004

Physica A: Statistical Mechanics and its Applications

230

141

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We present a study of information flow that takes into account the observation that an item relevant to one person is more likely to be of interest to individuals in the same social circle than those outside of it. This is due to the fact that the similarity of node attributes in social networks decreases as a function of the graph distance. An epidemic model on a scale-free network with this property has a finite threshold, implying that the spread of information is limited. We tested our predictions by measuring the spread of messages in an organization and also by numerical experiments that take into consideration the organizational distance among individuals.The problem of information flows in social organizations is relevant to issues of productivity, innovation and the sorting out of useful ideas out of the general chatter of a community. How information spreads determines the speed with which individuals can act and plan their future activities. In particular, email has become the predominant means of communication in the information society. It pervades business, social and scientific exchanges and as such it is a highly relevant area for research on communities and social networks. Not surprisingly, email has been established as an indicator of collaboration and knowledge exchange [1,2,3,4,5]. Email is also a good medium for research because it provides plentiful data on personal communication in an electronic form.Since individuals tend to organize both formally and informally into groups based on their common activities and interests, the way information spreads is affected by the topology of the interaction network, not unlike the spread of a disease among individuals. Thus one would expect that epidemic models on graphs are relevant to the study of information flow in organizations. In particular, recent work on epidemic propagation on scale free networks found that the threshold for an epidemic is zero, implying that a finite fraction of the graph becomes infected for arbitrarily low transmission probabilities [6,7,8]. The presence of additional network structure was found to further influence the spread of disease on scale-free graphs [9, 10, 11].There are, however, differences between information flows and the spread of viruses. While viruses tend to be indiscriminate, infecting any susceptible individual, information is selective and passed by its host only to individuals the host thinks would be interested in it.The information any individual is interested in depends strongly on their characteristics. Furthermore, individuals with similar characteristics tend to associate with one another, a phenomenon known as homophily [12,13,14]. Conversely, individuals many steps removed in a social network on average tend not to have as much in common, as shown in a study [15] of a network of Stanford student homepages and illustrated in Figure 1.We therefore introduce an epidemic model with decay in the transmission probability of a particular piece of in- formation as a function of the distance between t...

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Scale-free topology of e-mail networks

Cited by 804 publications

References 24 publications

Knowledge and networks: An experimental test of how network knowledge affects coordination

Knowledge and networks: An experimental test of how network knowledge affects coordination

The dynamics of a mobile phone network

Information flow in social groups

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