Paper year
2026
Paper dossier
A multimodal graph-based music auto-tagging framework: integrating social and content intelligence
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Source slug
unknown
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Run shadow-generalization-product-candidate-ranking-v1Top 50 surfaced
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Families present
2
Top 50
0
Run label
shadow-generalization-product-candidate-ranking-v1
Snapshot
source-snapshot-shadow-generalization-v1-20260521
Scope: family global | run rank-83787b91ef
Emerging
Present in run, outside top 50
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Signals: semantic=0.8778, citation_velocity=0.0000, topic_growth=0.0000, diversity_penalty=0.0000
Why this surfaced | 3 used | 1 penalty | 1 not computed
Embedding slice fit (corpus centroid)used
Embedding slice fit (corpus centroid): high; used in final ranking (contribution to score: 0.1756)
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0000)
Topic momentumused
Topic momentum: low; used in final ranking (contribution to score: 0.0000)
Cross-cluster signalnot computed
Cross-cluster signal: not computed for this run
Similarity penaltypenalty
Similarity penalty: reduces score when non-zero (contribution to score: 0.0000)
Bridge
Present in run, outside top 50
Multi-topic paper in active topics; no cluster_version on this run so bridge_score was not computed.
Signals: citation_velocity=0.0000, topic_growth=0.0000, diversity_penalty=1.0000
Why this surfaced | 2 used | 1 penalty | 2 not computed
Semantic matchnot computed
Semantic match: not computed for this run
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0000)
Topic momentumused
Topic momentum: low; used in final ranking (contribution to score: 0.0000)
Cross-cluster signalnot computed
Cross-cluster signal: not computed for this run
Topic breadth penaltypenalty
Topic breadth penalty: reduces score when non-zero (contribution to score: -0.2000)
Under-cited
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Abstract
Abstract With the rapid growth of music streaming platforms, effective music auto-tagging has become crucial for Music Information Retrieval (MIR) and recommendation. However, existing approaches face significant limitations: single-modality methods, which use only audio or text features, fail to capture the rich semantic diversity of music tags, while current multimodal approaches overlook critical interactive relationships beyond music content. Moreover, most studies ignore the co-occurrence dependencies among tags, which are essential for multi-label prediction. To address these challenges, we propose MuCoGraph, a novel multimodal graph-based hybrid learning framework for music auto-tagging. Our approach integrates multiple data modalities-lyrics, user comments, and audio spectrograms-with three heterogeneous graph neural networks: preference graphs that capture artist-listener interactions, group graphs that model content similarities, and tag co-occurrence graphs that learn label dependencies. The framework employs hierarchical co-attention mechanisms that enable cross-modal feature enhancement, allowing graph-based features to strengthen textual and audio representations through mutual learning. Experiments were conducted on a real-world dataset, which we integrated from multiple online platforms, demonstrating that MuCoGraph outperforms all the compared baseline methods in music auto-tagging. Notably, MuCoGraph achieves the most substantial improvements in top-12 recommendations, with relative gains of 12% in F1-score, 11.6% in NDCG, and 11.8% in MAP compared to the best baseline. This demonstrates progressively greater performance advantages as the recommendation scope increases, highlighting its enhanced ability to maintain quality across extended tag lists. These performance gains provide practical benefits for music platforms, including enhanced user engagement and more efficient content management processes. Furthermore, ablation studies demonstrate the critical contribution of each model component, particularly showing that graph-based features effectively improve both textual and audio representations through cross-modal enhancement.
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