GRAND-HC: Graph-Refined Author Name Disambiguation

2026-08-24

AI Summary

为解决作者姓名消歧问题，特别是长尾作者过度合并和聚类数估计不准确的问题，提出GRAND-HC框架，利用图注意力网络和对比学习优化作者表示，并通过轻量级模块实现准确的聚类数估计。

Abstract

From-Scratch Name Disambiguation (SND) groups papers sharing an ambiguous name into clusters of distinct real-world authors. Existing methods suffer from two critical limitations: (1) inherent long-tailed author distribution biases representation learning, causing over-merging of tail authors; (2) existing cluster number estimation methods are unreliable for long paper sequences, hindering large-scale deployment. We propose \textbf{GRAND-HC}, a complete end-to-end SND framework. We construct a heterogeneous paper graph via co-author, co-organization, and co-venue relations, using a graph attention network as the embedding backbone. \textbf{Harmony Contrastive Learning (HCL)} dynamically reweights training loss to suppress overfitting to prolific authors, learning discriminative embeddings. A \textbf{Graph-Refined Distance Matrix (GRDM)} leverages graph topology to optimize pairwise distances, further preventing tail author over-merging. Meanwhile, a lightweight \textbf{Paper Compression Module (PCM)} achieves accurate cluster number estimation across varying scales. Finally, Hierarchical Agglomerative Clustering outputs the final clusters. Extensive experiments demonstrate state-of-the-art macro F1 performance. GRAND-HC has been deployed in a billion-scale academic database. Source code: https://github.com/baokou-fw2/GRAND-HC.

Problem

• Name Disambiguation
• Long-tailed Distribution
• Cluster Estimation
• Representation Learning

Innovation

• Graph-Refined Distance Matrix
• Harmony Contrastive Learning
• Paper Compression Module