sesh
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Widespread machine learning methods behind ‘link prediction’ are performing very poorly, study shows
New research from UC Santa Cruz Professor of Computer Science and Engineering C. “Sesh” Seshadhri published in the journal Proceedings of the National Academy of Sciences establishes that the metric used to measure link prediction performance is missing crucial information, and link prediction tasks are performing significantly worse than popular literature indicates.
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Study shows widely used machine learning methods don’t work as claimed
Researchers demonstrated the mathematical impossibility of representing social networks and other complex networks using popular methods of ‘low-dimensional embeddings’.