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- Song, Han Hee, et al.
(författare)
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Clustered Embedding of Massive Social Networks
- 2012
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Ingår i: Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450310970 ; , s. 331-342
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The explosive growth of social networks has created numerous exciting research opportunities. A central concept in the analysis of social networks is a proximity measure, which captures the closeness or similarity between nodes in a social network. Despite much research on proximity measures, there is a lack of techniques to eciently and accurately compute proximity measures for large-scale social networks. In this paper, we develop a novel dimensionality reduction technique, called clustered spectral graph embedding, to embed the graphs adjacency matrix into a much smaller matrix. The embedded matrix together with the embedding subspaces capture the essential clustering and spectral structure of the original graph and allows a wide range of analysis tasks to be performed in an ecient and accurate fashion. To evaluate our technique, we use three large real-world social network datasets: Flickr, LiveJournal and MySpace, with up to 2 million nodes and 90 million links. Our results clearly demonstrate the accuracy, scalability and exibility of our approach in the context of three importantsocial network analysis tasks: proximity estimation, missing link inference, and link prediction.
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