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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Sparse Coding for Key Node Selection over Networks
verfasst von : Ye Xu, Dan Rockmore
Erschienen in: Discovery Science
Verlag: Springer International Publishing
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The size of networks now needed to model real world phenomena poses significant computational challenges.
Key node selection in networks
, (KNSIN) presented in this paper, selects a representative set of nodes that preserves the sketch of original nodes in the network and thus, serves as a useful solution to this challenge. KNSIN is accomplished via a sparse coding algorithm that efficiently learns a basis set over the feature space defined by the nodes. By executing a stop criterion, KNSIN automatically learns the dimensionality of the node space and guarantees that the learned basis accurately preserves the sketch of the original node space. In experiments, we use two large scale network datasets to evaluate the proposed KNSIN framework. Our results on the two datasets demonstrate the effectiveness of the KNSIN algorithm.