GNNs vs. MLPs: Predicting Artist Success in Music Networks

A new study published on arXiv evaluates the effectiveness of Graph Neural Networks (GNNs) in predicting artist success within collaboration networks. The research introduces a dataset for the Polish music scene and compares it with existing Italian and Danish networks. While GNNs showed comparable performance to Multilayer Perceptrons (MLPs) in some instances, MLPs generally proved superior for success prediction, suggesting that artist attributes like genre and label affiliation may be more predictive than network topology alone. AI

IMPACT Suggests that artist attributes may be more predictive of success than network topology in music collaboration.

RANK_REASON The cluster contains an academic paper detailing a new evaluation of GNNs for a specific prediction task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• Danish networks
• graph neural networks
• Hugging Face
• Italian networks
• Multilayer Perceptron
• Polish music scene
• Wiktor Dowgiałło

• paper
• other

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