部分标记网络中基于相似性的分类
Similarity-Based Classification in Partially Labeled Networks
摘要：
我们提出了一种基于相似性的方法，利用节点之间的相似性，来解决部分标记网络中的分类问题。基本假设是，如果两个节点更相似，则它们更有可能被归类为同一类。本文介绍了10个相似性指标，包括5个局部指标和5个全局指标。在政治类图书联合购买网络上的实验结果表明，即使在标签节点稀疏的情况下，基于相似度的分类方法仍能给出较高的分类精度，而稀疏是分类的难点之一。此外，我们发现，当目标网络有许多标记的节点，局部索引可以表现得像那些全局索引一样好，而当数据稀疏的全局索引表现得更好。此外，基于相似性的方法可以在一定程度上克服分类中的另一个难点--不一致性问题。
英文摘要：
We propose a similarity-based method, using the similarity between nodes, to address the problem of classification in partially labeled networks. The basic assumption is that two nodes are more likely to be categorized into the same class if they are more similar. In this paper, we introduce ten similarity indices, including five local ones and five global ones. Empirical results on the co-purchase network of political books show that the similarity-based method can give high accurate classification even when the labeled nodes are sparse which is one of the difficulties in classification. Furthermore, we find that when the target network has many labeled nodes, the local indices can perform as good as those global indices do, while when the data is sparce the global indices perform better. Besides, the similarity-based method can to some extent overcome the unconsistency problem which is another difficulty in classification.
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。
