MYE：学术社交网络中的缺失年份估计
MYE: Missing Year Estimation in Academic Social Networks
摘要：
在文献计量学研究中，一个常见的挑战是如何处理不正确或不完整的数据。然而，给定大量数据，数据项之间通常存在某些关系，这些关系可以允许我们恢复丢失的数据项并纠正错误数据。在本文中，我们研究了这种特殊的问题-估计与出版物相关的缺失年份信息（以及作者的活跃出版年份）。我们首先提出了一个简单的算法，只利用“直接”的信息，如论文引用/参考文献关系或论文作者关系。这个简单算法的结果被用作比较的基准。我们的目标是开发算法，提高覆盖率（丢失年份论文的百分比恢复）和准确性（估计年份与实际年份的平均绝对误差）。我们提出了一些先进的算法，扩展推理的信息传播。对于每种算法，我们根据给定的学术社交网络类型提出了三个版本：a）同质（仅包含论文引用链接），b）二分（仅包含论文-作者关系）和c）异质（论文引用和论文-作者关系）。我们在三个公共数据集（MSR Libra，DBLP和APS）上进行了实验，并通过应用K-fold交叉验证方法进行了评估。我们表明，先进的算法可以提高覆盖率和准确性。
英文摘要：
In bibliometrics studies, a common challenge is how to deal with incorrect or incomplete data. However, given a large volume of data, there often exists certain relationships between the data items that can allow us to recover missing data items and correct erroneous data. In this paper, we study a particular problem of this sort - estimating the missing year information associated with publications (and hence authors' years of active publication). We first propose a simple algorithm that only makes use of the "direct" information, such as paper citation/reference relationships or paper-author relationships. The result of this simple algorithm is used as a benchmark for comparison. Our goal is to develop algorithms that increase both the coverage (the percentage of missing year papers recovered) and accuracy (mean absolute error of the estimated year to the real year). We propose some advanced algorithms that extend inference by information propagation. For each algorithm, we propose three versions according to the given academic social network type: a) Homogeneous (only contains paper citation links), b) Bipartite (only contains paper-author relations), and, c) Heterogeneous (both paper citation and paper-author relations). We carry out experiments on the three public data sets (MSR Libra, DBLP and APS), and evaluated by applying the K-fold cross validation method. We show that the advanced algorithms can improve both coverage and accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
