This paper studies a link-text algorithm to model scientific documents by citation influences, which is applied to document clustering and influence prediction. Most existing link-text algorithms ignore the different weights of citation influences that cited documents have on the corresponding citing document. In fact, citation influences reveal the latent structure of citation networks which is more accurate to describe the knowledge flow than the original citation structure. In this study, a citation influence is modeled as a weight of linear combination that approximates the text of a document by the content of its citations. Then, we present a novel matrix factorization algorithm, called Citation-Influences-Text Nonnegative Matrix Factorization (CIT-NMF), which incorporates text and citations to obtain better document representations by learning influence weights. In addition, an efficient optimization method is derived to solve the optimization problem. Experimental results on several real datasets show satisfactory improvements over the baseline models.

中文翻译：

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利用引文影响对科学文件进行建模

本文研究了一种通过引用影响对科学文献进行建模的链接文本算法，并将其应用于文献聚类和影响预测。大多数现有的链接文本算法都忽略引用文档对相应引用文档的引用影响的不同权重。实际上，引文影响揭示了引文网络的潜在结构，与原始引文结构相比，它更能准确地描述知识流。在本研究中，引用影响被建模为线性组合的权重，该线性组合的权重通过文档的引用内容近似于文档的文本。然后，我们提出了一种新颖的矩阵分解算法，称为Citation-Influences-Text非负矩阵分解（CIT-NMF），它结合了文本和引文，通过学习影响力权重来获得更好的文档表示形式。另外，推导了一种有效的优化方法来解决优化问题。在几个真实数据集上的实验结果表明，在基线模型上有令人满意的改进。