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Growth of the flickr social network

Published: 18 August 2008 Publication History

Abstract

Online social networking sites like MySpace, Orkut, and Flickr are among the most popular sites on the Web and continue to experience dramatic growth in their user population. The popularity of these sites offers a unique opportunity to study the dynamics of social networks at scale. Having a proper understanding of how online social networks grow can provide insights into the network structure, allow predictions of future growth, and enable simulation of systems on networks of arbitrary size. However, to date, most empirical studies have focused on static network snapshots rather than growth dynamics.
In this paper, we collect and examine detailed growth data from the Flickr online social network, focusing on the ways in which new links are formed. Our study makes two contributions. First, we collect detailed data covering three months of growth, encompassing 950,143 new users and over 9.7 million new links, and we make this data available to the research community. Second, we use a first-principles approach to investigate the link formation process. In short, we find that links tend to be created by users who already have many links, that users tend to respond to incoming links by creating links back to the source, and that users link to other users who are already close in the network.

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Published In

cover image ACM Conferences
WOSN '08: Proceedings of the first workshop on Online social networks
August 2008
92 pages
ISBN:9781605581828
DOI:10.1145/1397735
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 August 2008

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Author Tags

  1. growth
  2. measurement
  3. social networks

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  • Research-article

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SIGCOMM '08
Sponsor:
SIGCOMM '08: ACM SIGCOMM 2008 Conference
August 18, 2008
WA, Seattle, USA

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Overall Acceptance Rate 12 of 36 submissions, 33%

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  • (2024)Fast Personalized PageRank for Customized Analysis Range Using Static Index2024 Fifteenth International Conference on Ubiquitous and Future Networks (ICUFN)10.1109/ICUFN61752.2024.10625115(304-309)Online publication date: 2-Jul-2024
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  • (2023)Real-Time PageRank on Dynamic GraphsProceedings of the 32nd International Symposium on High-Performance Parallel and Distributed Computing10.1145/3588195.3593004(239-251)Online publication date: 7-Aug-2023
  • (2023)A Higher-Order Temporal H-Index for Evolving NetworksProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599242(1770-1782)Online publication date: 6-Aug-2023
  • (2023)Hyperbolic Temporal Network EmbeddingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.323239835:11(11489-11502)Online publication date: 1-Nov-2023
  • (2023)Maximum Flow on Highly Dynamic Graphs2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386845(522-529)Online publication date: 15-Dec-2023
  • (2022)Users Holding Accounts on Multiple Online Social NetworksResearch Anthology on Usage, Identity, and Impact of Social Media on Society and Culture10.4018/978-1-6684-6307-9.ch020(397-416)Online publication date: 10-Jun-2022
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