Social network analysis (SNA) has attracted a lot of attention in several domains in the past decades. It can be of 2-folds: one is content-based, and another one is structured-based analysis. Link prediction is one of the emerging research problems, which comes under structured-based analysis that deals with predicting the missing link, which is likely to appear in the future. In this article, the supervised machine learning techniques have been implemented to predict the possibilities of establishing the links in future. The major contribution in this article lies in feature construction from the topological structure of the network. Several structured-based similarity measures have been considered for preparing the feature vector for each nonexisting links in the network. The performance of the proposed algorithm has been extensively validated by comparing with other link prediction algorithms using both real-world and synthetic data sets.

中文翻译：

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社交网络中基于结构化特征提取的监督链接预测

在过去的几十年中，社交网络分析（SNA）在多个领域引起了广泛关注。它可以有两种：一种是基于内容的，另一种是基于结构的分析。链接预测是新兴的研究问题之一，它属于基于结构化的分析，处理预测未来可能出现的缺失链接。在本文中，已实施监督机器学习技术来预测未来建立链接的可能性。本文的主要贡献在于从网络的拓扑结构构建特征。已经考虑了几种基于结构化的相似性度量来为网络中每个不存在的链接准备特征向量。