Measurement and Analysis of Online Social Networks

Measurement and Analysis of Online Social Networks (2007)

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by Alan Mislove , Massimiliano Marcon , Krishna P. Gummadi , Peter Druschel , Bobby Bhattacharjee

Venue:	In Proceedings of the 5th ACM/USENIX Internet Measurement Conference (IMC’07
Citations:	185 - 12 self

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Datum	Value	Source
TITLE	Measurement and Analysis of Online Social Networks	INFERENCE
AUTHOR NAME	Alan Mislove	SVM HeaderParse 0.2
AUTHOR AFFIL	† Max Planck Institute for Software Systems	SVM HeaderParse 0.2
AUTHOR NAME	Massimiliano Marcon	SVM HeaderParse 0.2
AUTHOR NAME	Krishna P. Gummadi	SVM HeaderParse 0.2
AUTHOR NAME	Peter Druschel	SVM HeaderParse 0.2
AUTHOR NAME	Bobby Bhattacharjee	SVM HeaderParse 0.2
AUTHOR AFFIL	Measurement	SVM HeaderParse 0.2
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 scalefree 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.	SVM HeaderParse 0.2
YEAR	2007	INFERENCE
VENUE	In Proceedings of the 5th ACM/USENIX Internet Measurement Conference (IMC’07	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	48 found	ParsCit 1.0