Cluster Algebras: Network Science and Machine Learning
This work applies data science techniques to mathematical structures, which is incremental for researchers in mathematics and theoretical physics.
The paper tackled the problem of analyzing cluster algebras using network science and machine learning, resulting in the discovery of symmetry in quiver exchange graphs and achieving classification accuracies exceeding 0.9 for cluster algebras.
Cluster algebras have recently become an important player in mathematics and physics. In this work, we investigate them through the lens of modern data science, specifically with techniques from network science and machine learning. Network analysis methods are applied to the exchange graphs for cluster algebras of varying mutation types. The analysis indicates that when the graphs are represented without identifying by permutation equivalence between clusters an elegant symmetry emerges in the quiver exchange graph embedding. The ratio between number of seeds and number of quivers associated to this symmetry is computed for finite Dynkin type algebras up to rank 5, and conjectured for higher ranks. Simple machine learning techniques successfully learn to classify cluster algebras using the data of seeds. The learning performance exceeds 0.9 accuracies between algebras of the same mutation type and between types, as well as relative to artificially generated data.