Originally published in US Santa Cruz NEWSCENTER, February 12, 2024
New research indicates that methods used to test the accuracy of link prediction are flawed, and that link prediction does not work as well as common benchmarking tests currently indicate
As you scroll through any social media feed, you are likely to be prompted to follow or friend another person, expanding your personal network and contributing to the growth of the app itself. The person suggested to you is a result of link prediction: a widespread machine learning (ML) task that evaluates the links in a network — your friends and everyone else’s — and tries to predict what the next links will be.
Beyond being the engine that drives social media expansion, link prediction is also used in a wide range of scientific research, such as predicting the interaction between genes and proteins, and is used by researchers as a benchmark for testing the performance of new ML algorithms.
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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