The transfer learning methodology leverages knowledge from the source domain with abundant training data to the insufficient target domain. Recently, new approaches continue to be developed and used to solve different classification tasks, ranging from public news to videos and to many others. Most transfer learning methods are based on the assumption that both source and target data are in the same feature space or with the same data distribution, which however is not always true in real applications where it would lead to a negative transfer. In order to overcome this hurdle, the multiple-source transfer learning framework is useful. Since many real systems can be represented by networks, how to utilize the structural similarity between different networks so as to increase the transfer effectiveness becomes important. In this paper, the NES specification index is used to quantitatively measure the structural similarity between two networks, based on which a new transfer learning method (named NES-TL) is developed. Experiments on tag popularity prediction in StackExchange Q&A communities verify the effectiveness of the proposed approach, showing that it behaves better than existing baseline methods.

中文翻译：

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NES-TL：基于网络嵌入相似性的迁移学习

迁移学习方法利用来自具有丰富训练数据的源域的知识到不足的目标域。最近，不断开发和使用新方法来解决不同的分类任务，从公共新闻到视频等等。大多数迁移学习方法都基于这样的假设：源数据和目标数据都位于相同的特征空间或具有相同的数据分布，然而在实际应用中并不总是如此，因为它会导致负迁移。为了克服这个障碍，多源迁移学习框架很有用。由于许多真实系统都可以用网络来表示，因此如何利用不同网络之间的结构相似性来提高传输效率就变得很重要。在本文中，NES规范指数​​用于定量衡量两个网络之间的结构相似性，在此基础上开发了一种新的迁移学习方法（命名为NES-TL）。StackExchange Q&A 社区中标签流行度预测的实验验证了所提出方法的有效性，表明它比现有的基线方法表现得更好。