Poster (2 pages)
Official ACM published version: http://dx.doi.org/10.1145/2009916.2010136
Author's version: PDF (103KB)
Unlike a traditional social network service, a microblogging network like Twitter is a hybrid network, combining aspects of both social networks and information networks. Understanding the structure of such hybrid networks and to predict new links are important for many tasks such as friend recommendation, community detection, and network growth models. In this paper, by analyzing data collected over time, we find that 90% of new links are to people just two hops away and dynamics of friend acquisition are also related to users' account age. Finally, we compare two popular sampling methods which are widely used for network analysis and find that ForestFire does not preserve properties required for the link prediction task.
In Proceedings of the 34th Annual ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1235-1236, Beijing, China, ACM Press, July 2011.
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