Big Data Approaches to Knot Theory: Understanding the Structure of the Jones Polynomial
This provides a new mathematical framework for analyzing knot invariants, though it appears to be an incremental advancement in applying data science methods to theoretical mathematics.
The researchers tackled the problem of understanding the structure of the Jones polynomial in knot theory by applying manifold learning techniques to a dataset of over 10 million knots. They found that the Jones polynomial can be viewed as an approximately 3-dimensional manifold, with this description remaining stable across different crossing numbers.
We examine the structure and dimensionality of the Jones polynomial using manifold learning techniques. Our data set consists of more than 10 million knots up to 17 crossings and two other special families up to 2001 crossings. We introduce and describe a method for using filtrations to analyze infinite data sets where representative sampling is impossible or impractical, an essential requirement for working with knots and the data from knot invariants. In particular, this method provides a new approach for analyzing knot invariants using Principal Component Analysis. Using this approach on the Jones polynomial data we find that it can be viewed as an approximately 3 dimensional manifold, that this description is surprisingly stable with respect to the filtration by the crossing number, and that the results suggest further structures to be examined and understood.