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Measurement and Analysis of Online Social Networks

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Mislove,  Alan
Group P. Druschel, Max Planck Institute for Software Systems, Max Planck Society;

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Marcon,  Massimiliano
Group K. Gummadi, Max Planck Institute for Software Systems, Max Planck Society;

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Gummadi,  Krishna P.
Group K. Gummadi, Max Planck Institute for Software Systems, Max Planck Society;

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Druschel,  Peter
Group P. Druschel, Max Planck Institute for Software Systems, Max Planck Society;

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Citation

Mislove, A., Marcon, M., Gummadi, K. P., Druschel, P., & Bhattacharjee, B. (2007). Measurement and Analysis of Online Social Networks. In IMC'07: proceedings of the 2007 ACM SIGCOMM (pp. 29-42). New York, NY, USA: ACM.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0028-8C73-E
Abstract
Online social networking sites like Orkut, YouTube, and Flickr are among the most popular sites on the Internet. Users of these sites form a social network, which provides a powerful means of sharing, organizing, and finding content and contacts. The popularity of these sites provides an opportunity to study the characteristics of online social network graphs at large scale. Understanding these graphs is important, both to improve current systems and to design new applications of online social networks. This paper presents a large-scale measurement study and analysis of the structure of multiple online social networks. We examine data gathered from four popular online social networks: Flickr, YouTube, LiveJournal, and Orkut. We crawled the publicly accessible user links on each site, obtaining a large portion of each social network’s graph. Our data set contains over 11.3 million users and 328 million links. We believe that this is the first study to examine multiple online social networks at scale. Our results confirm the power-law, small-world, and scale-free properties of online social networks. We observe that the indegree of user nodes tends to match the outdegree; that the networks contain a densely connected core of high-degree nodes; and that this core links small groups of strongly clustered, low-degree nodes at the fringes of the network. Finally, we discuss the implications of these structural properties for the design of social network based systems.