Short Paper (6 pages)
Official ACM published version: http://dx.doi.org/10.1145/2063576.2063743
Author's version: PDF (401KB)
With hundreds of millions of participants, social media services have become commonplace. 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 predicting new links are important for many tasks such as friend recommendation, community detection, and modeling network growth. We note that the link prediction problem in a hybrid network is different from previously studied networks. Unlike the information networks and traditional online social networks, the structures in a hybrid network are more complicated and informative. We compare most popular and recent methods and principles for link prediction and recommendation. Finally we propose a novel structure-based personalized link prediction model and compare its predictive performance against many fundamental and popular link prediction methods on real-world data from the Twitter microblogging network. Our experiments on both static and dynamic data sets show that our methods noticeably outperform the state-of-the-art.
In Proceedings of the 20th ACM Conference on Information and Knowledge Management (CIKM), pages 1163-1168, Glasgow, Scotland, October 2011.
© ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution.
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