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Clustered Embedding of Massive Social Networks

By Han Hee Song, Berkant Savas, Tae Won Cho, Vacha Dave, Zhengdong Lu, Inderjit S. Dhillon, Yin Zhang and Lili Qiu


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 the network. Despite much research on proximity measures, there is a lack of techniques to efficiently and accurately compute proximity measures for largescale social networks. In this paper, we embed the original massive social graph into a much smaller graph, using a novel dimensionality reduction technique termed Clustered Spectral Graph Embedding. We show that the embedded graph captures the essential clustering and spectral structure of the original graph and allow a wide range of analysis to be performed on massive social graphs. Applying the clustered embedding to proximity measurement of social networks, we develop accurate, scalable, and flexible solutions to three important social network analysis tasks: proximity estimation, missing link inference, and link prediction. We demonstrate the effectiveness of our solutions to the tasks in the context of large real-world social network datasets: Flickr, LiveJournal, and My-Space with up to 2 million nodes and 90 million links

Topics: Categories and Subject Descriptors H.3.5 [Information Storage and Retrieval, Online Information Services—Web-based services, J.4[Computer Applications, Social and Behavioral Sciences—Sociology General Terms Algorithms, Human Factors, Measurement Keywords Social Network, Graph Clustering, Graph Embedding, Proximity Estimation, Missing Link Inference, Link Prediction
Year: 2013
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
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