Reconstructing Randomized Social Networks
SDM(2010)
摘要
In social networks, nodes correspond to entities and edges to links between them. In most of the cases, nodes are also associated with a set of features. Noise, missing values or efforts to preserve privacy in the network may transform the original network G and its feature vectors F. This transformation can be modeled as a randomization method. Here, we address the problem of reconstructing the original network and set of features given their randomized counterparts Gand Fand knowledge of the randomization model. We identify the cases in which the original network G and feature vectors F can be reconstructed in polynomial time. Finally, we illustrate the efficacy of our methods using both generated and real datasets.
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关键词
social-network analysis,privacy- preserving data mining,feature vector,social network,missing values,polynomial time,social network analysis
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