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Information Extraction from Large-Multi-Layer Social Networks

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University of Michigan - Ann Arbor Ann Arbor United States

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Social networks often encode community structure using multiple distinct types of links between nodes. In this paper we introduce a novel method to extract information from such multi-layer networks, where each type of link forms its own layer. Using the concept of Pareto optimality, community detection in this multi-layer setting is formulated as a multiple criterion optimization problem. We propose an algorithm for finding an approximate Pareto frontier containing a family of solutions. The power of this approach is demonstrated on a Twitter dataset, where the nodes are hashtags and the layers correspond to 1 behavioral edges connecting pairs of hashtags whose temporal profiles are similar and 2 relational edges connecting pairs of hashtags that appear in the same tweets.

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Journal Article

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2015 IEEE International Conference on Acoustics, Speech, and Signal Processing , 19 Apr 2015, 24 Apr 2015, Presented at the IEEE International Conference on Acoustics, Speech and Signal Processing (40th) (ICASSP 2015) held in Brisbane, Australia on 19-24 April 2015.




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Approved For Public Release;

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