评估图神经网络（GNN）在艺术家合作网络成功预测中的应用
Evaluating GNNs for Success Prediction in Artist Collaboration Networks
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- IT University of Copenhagen（哥本哈根信息技术大学）
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中文总结 AI 辅助
本研究引入波兰音乐场景数据集，基于元数据与网络位置测试GNN在艺术家流行度预测中的效能，发现GNN宏F1分数与MLP相当但成功指标更优，内部节点特征预测能力强于网络拓扑。
中文摘要 AI 辅助
随着音乐产业日益成为一项合作性事业，理解艺术家网络的底层结构已成为文化数据分析的焦点。本研究在先前对意大利和丹麦网络分析的基础上，引入了波兰音乐场景的新数据集。通过利用先前研究中使用的方法，本研究实现了对三个不同欧洲音乐场景的直接比较，并允许将创建的网络合并为一个网络。此外，本研究提出了一个框架，用于测试图神经网络（GNN）基于元数据和网络位置进行艺术家流行度预测的效能。统计分析显示，波兰网络和跨国网络表现出相似的属性和聚类行为，与先前的模型一致。对预测架构的评估表明，尽管在特定情况下GNN模型的宏F1分数与多层感知机（MLP）相当，但在成功指标方面，MLP仍是更优的模型。结果表明，内部节点特征（如流派和唱片公司归属）可能比网络拓扑结构具有更强的预测能力。GNN模型在跨国网络中的更高性能还表明，当网络跨越多个语言和地理边界时，关系特征会变得更具信息量，而GNN可能能够捕获合并网络之间复杂的“桥梁”结构。
英文摘要
As the music industry becomes an increasingly collaborative effort, understanding the underlying structures of the artist network has become a focal point in cultural data analytics. This study expands on the previous analyses of the Italian and Danish networks by introducing a novel dataset of the Polish music scene. By utilizing methodologies used in the prior studies, this work enables a direct comparison between three distinct European music landscapes and allows to merged the created networks into one. Furthermore, this research introduces a framework to test the efficacy of Graph Neural Networks (GNNs) for artist popularity predictions based on the metadata and the position in the network. The statistical analysis revealed that the Polish and tri-national network exhibit similar properties and clustering behaviours, consistent with prior models. An evaluation of the predictive architectures reveals that while GNN models achieve a comparable F1-macro scores to the Multilayer Perceptron (MLP) in specific cases however the MLP remains a superior model regarding the success metric. The results suggest that internal node features - such as genre and label affiliation might carry more predictive capabilities than the topology of the network. The higher performance of the GNN models in the tri-national network might also suggests that the relational features become more informative when the network spans multiple linguistic and geographic boundaries, with the GNNs potentially capturing complex 'bridge' structures between the merged networks.
