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The graphs built by our proposed method, ABC-LSH, are created up to 15× faster than baselines, while performing just as well in classification-based evaluation.
Discovering and analyzing networks from non-network data is a task with applications in fields as diverse as neuroscience, genomics, energy, economics, ...
Discovering and analyzing networks from non-network data is a task with applications in fields as diverse as neuroscience, genomics, energy, economics, and more ...
This work proposes a fast network discovery approach based on probabilistic hashing of randomly selected time series subsequences that construct graphs ...
This repository hosts the code for our IEEE ICDM 2017 paper and follow-up KAIS journal paper on inferring networks from time series efficiently: Tara Safavi, ...
Discovering and analyzing networks from non-network data is a task with applications in fields as diverse as neuroscience, genomics, energy, economics, ...
Over the years, researchers have used brain graphs to encode the correlations of brain activities and uncover interesting connectivity patterns between ...
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Discovering and analyzing networks from non-network data is a task with applications in fields as diverse as neuroscience, genomics, energy, economics, ...
Scalable Hashing-Based Network Discovery. Safavi, T; Sripada, C; Koutra, D. Safavi, T (reprint author), Univ Michigan, Comp Sci & Engn, Ann Arbor, MI 48109 ...
Scalable Hashing-based Network Discovery Tara Safavi, Chandra Sripada, Danai Koutra In IEEE International Conference on Data Mining (ICDM), November 2017