Structure and evolution of online social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph evolution: Densification and shrinking diameters
ACM Transactions on Knowledge Discovery from Data (TKDD)
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Proceedings of the first workshop on Online social networks
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Power-Law Distributions in Empirical Data
SIAM Review
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Structural Predictors of Tie Formation in Twitter: Transitivity and Mutuality
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Analyzing patterns of information cascades based on users' influence and posting behaviors
Proceedings of the 2nd Temporal Web Analytics Workshop
Characterizing topic-specific hashtag cascade in twitter based on distributions of user influence
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Semantics + filtering + search = twitcident. exploring information in social web streams
Proceedings of the 23rd ACM conference on Hypertext and social media
We love rock 'n' roll: analyzing and predicting friendship links in Last.fm
Proceedings of the 3rd Annual ACM Web Science Conference
Predicting aggregate social activities using continuous-time stochastic process
Proceedings of the 21st ACM international conference on Information and knowledge management
WiseMarket: a new paradigm for managing wisdom of online social users
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 22nd international conference on World Wide Web companion
Groundhog day: near-duplicate detection on Twitter
Proceedings of the 22nd international conference on World Wide Web
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Understanding a network's temporal evolution appears to require multiple observations of the graph over time. These often expensive repeated crawls are only able to answer questions about what happened from observation to observation, and not what happened before or between network snapshots. Contrary to this picture, we propose a method for Twitter's social network that takes a single static snapshot of network edges and user account creation times to accurately infer when these edges were formed. This method can be exact in theory, and we demonstrate empirically for a large subset of Twitter relationships that it is accurate to within a few hours in practice. We study users who have a very large number of edges or who are recommended by Twitter. We examine the graph formed by these nearly 1,800 Twitter celebrities and their 862 million edges in detail, showing that a single static snapshot can give novel insights about Twitter's evolution. We conclude from this analysis that real-world events and changes to Twitter's interface for recommending users strongly influence network growth.