The vast amount of data is key challenge to mine a new scholar that is plausible to be star in the upcoming period. The enormous amount of unstructured data raise every year is infeasible for traditional learning; consequently, we need a high quality of preprocessing technique to expand the performance of traditional learning. We have persuaded a novel approach, Authors classification algorithm using Transfer Learning (ACTL) to learn new task on target area to mine the external knowledge from the source domain. Comprehensive experimental outcomes on real‐world networks showed that ACTL, Node‐based Influence Predicting Stars, Corresponding Authors Mutual Influence based on Predicting Stars, and Specific Topic Domain‐based Predicting Stars enhanced the node classification accuracy as well as predicting rising stars to compared with contemporary baseline methods.

中文翻译：

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在共同作者网络中使用转移学习和预测星标对作者进行分类

挖掘大量数据是挖掘一名新学者的关键挑战，该学者有望在未来一段时间内成为明星。每年要收集大量的非结构化数据对于传统学习是不可行的；因此，我们需要高质量的预处理技术来扩展传统学习的性能。我们说服了一种新颖的方法，即使用转移学习（ACTL）的作者分类算法来学习目标区域上的新任务，以从源域中挖掘外部知识。实际网络上的综合实验结果表明，基于节点的影响力预测星ACTL，基于预测星号的通讯作者相互影响，