AI 中文总结
研究针对作者名消歧的两大局限，提出端到端框架GRAND-HC，结合图注意力网络、HCL、GRDM和PCM等技术，实现最优宏观F1并部署于十亿级学术数据库。
AI 中文摘要
从头开始的作者名消歧（SND）将共享模糊姓名的论文分组为不同现实世界作者的簇。现有方法存在两个关键局限：（1）长尾作者分布固有偏差会影响表示学习，导致尾部作者过度合并；（2）现有簇数估计方法对长论文序列不可靠，阻碍大规模部署。我们提出GRAND-HC，一个完整的端到端SND框架。我们通过合著者、合著机构和合著会议关系构建异质论文图，采用图注意力网络作为嵌入骨干。和谐对比学习（HCL）动态重新加权训练损失以抑制高产作者的过拟合，学习判别性嵌入。图优化距离矩阵（GRDM）利用图拓扑优化成对距离，进一步防止尾部作者过度合并。同时，轻量级论文压缩模块（PCM）实现不同规模下的准确簇数估计。最后，层次凝聚聚类输出最终簇。大量实验表明其达到最先进的宏观F1性能。GRAND-HC已部署在十亿级学术数据库中。源代码：this https URL。
英文摘要
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.
Journal refIntelligent Data Analysis (2026)
