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arXiv Machine Learning By Wiktor Dowgia{\l}{\l}o

Evaluating GNNs for Success Prediction in Artist Collaboration Networks

Read the original on arXiv Machine Learning →

The paper evaluates Graph Neural Networks (GNNs) for predicting artist success within collaboration networks, extending prior work on Italian and Danish music scenes by adding a Polish dataset and merging the three into a tri‑national network. Statistical analysis shows the Polish and combined networks share similar clustering properties, while predictive experiments reveal that GNNs match or slightly outperform a Multilayer Perceptron (MLP) in some cases but the MLP generally yields higher success metrics. The findings suggest that internal node attributes such as genre and label affiliation may be more predictive than network topology, and that GNNs may better capture cross‑border relational structures in the merged network.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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