AI 中文总结
该研究提出基于随机特征嵌入与投影的高效二次熵近似方法，结合摊销与控制变量实现大规模计算，在Open Graph Benchmark数据集上验证了其可识别跨学科影响力对象的能力。
AI 中文摘要
我们详细阐述了针对任意分布p和常见负型距离d的二次熵pᵀdp的可扩展近似方法，重点关注欧氏距离和球面测地线距离两种情形，二者均采用随机特征嵌入与投影，在简单框架内大幅降低计算复杂度。当d固定而p变化时，通过单次大矩阵乘法的摊销及控制变量法，可在低内存与低运行时间下实现大规模计算。我们在Open Graph Benchmark数据集上，将该方法与直接对采样及文献计量/科学计量示例进行对比，仅利用引用与文本特征，即可识别出具有极窄或极宽跨学科影响力的论文、领域及机构。
英文摘要
We detail scalable methods for approximating the quadratic entropy $p^T d p$ for arbitrary distributions $p$ and common distances $d$ of negative type. We focus on the Euclidean and spherical geodesic cases, which both use random feature embeddings and projections to dramatically improve computational complexity within a simple framework. Amortization of a single large matrix multiplication and control variates further enable computation at large scale with low memory and runtime in situations where $d$ is held constant while $p$ varies. We demonstrate this with a comparison against direct pair sampling and bibliometric/scientometric examples on Open Graph Benchmark datasets, revealing papers, fields, and institutions with both particularly narrow and broad interdisciplinary reach from their citations and text features alone.
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