Graph Learning

Clustering of cryptocurrencies

Graph learning from data, essential for interpretability and identification of the relationships among data, is a canonical problem that has received substantial attention in the literature. In general, learning a graph with a specific structure is an NP-hard combinatorial problem and thus designing a general tractable algorithm is challenging. Some useful structured graphs include connected, sparse, multi-component, bi-partite, and regular graphs. We focus on the development of efficient algorithms for practical deployment. An open source R package containing the code for all the experiments is available at


  • R package spectralGraphTopology: Learning Graphs from Data via Spectral Constraints