  Artist similarity plays an important role in organizing, understanding, and
subsequently, facilitating discovery in large collections of music. In this
paper, we present a hybrid approach to computing similarity between artists
using graph neural networks trained with triplet loss. The novelty of using a
graph neural network architecture is to combine the topology of a graph of
artist connections with content features to embed artists into a vector space
that encodes similarity. To evaluate the proposed method, we compile the new
OLGA dataset, which contains artist similarities from AllMusic, together with
content features from AcousticBrainz. With 17,673 artists, this is the largest
academic artist similarity dataset that includes content-based features to
date. Moreover, we also showcase the scalability of our approach by
experimenting with a much larger proprietary dataset. Results show the
superiority of the proposed approach over current state-of-the-art methods for
music similarity. Finally, we hope that the OLGA dataset will facilitate
research on data-driven models for artist similarity.
