A Comprehensive Survey on Learning Based Methods for Link Prediction Problem

S. Balvir, M. Raghuwanshi, Kavita R. Singh
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引用次数: 0

Abstract

The development of internet technology is continuously increasing day by day. Social network is also the internet based technology, running online to deliver more services and strengthen communities. The availability of large amount of data encouraged the various field of study like data science, data mining and analysis, neural network etc. Currently, in real scenario the two giants Facebook and Google have the tremendous amount of data. The link prediction problem is a key issue with social network analysis. The Link prediction issue is to predicting new link or future link between two nodes in a network. This paper talked about the several method used to address the issue of link prediction based on learning approach mainly it focus on supervised, unsupervised and semi supervised learning based methods.
基于学习的链路预测方法综述
网络技术的发展日益迅速。社交网络也是基于互联网的技术,在网上运行以提供更多的服务和加强社区。大量数据的可用性鼓励了数据科学、数据挖掘和分析、神经网络等各个领域的研究。目前,在现实场景中,Facebook和Google这两大巨头拥有海量的数据。链接预测问题是社交网络分析中的一个关键问题。链路预测问题是对网络中两个节点之间的新链路或未来链路进行预测。本文讨论了基于学习方法的链路预测问题的几种方法,重点介绍了基于监督、无监督和半监督学习的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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