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An Experiment with Link Prediction in Social Network: Two New Link Prediction Methods

机译：社交网络中链路预测的实验：两个新的链路预测方法

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摘要

This paper investigates link prediction methods in social networks (Facebook) and discusses two new link prediction methods. The two methods are structural based methods, which implies they are not based on any content of user profiles, but instead they are based on connections of the users in the social network. These two methods are the Common Neighbors of Neighbors and Node Connectivity prediction methods. The first introduced method can be considered as an extension to the Common Neighbors link prediction method. The second method is based on average connections of neighbors. Both methods are discussed in this paper and have been used in experiment. Additionally, Formulas, explanation, Pseudocode and an example is included about some preexisting methods of link prediction and the introduced methods considered in this paper. This paper also includes detailed applications of link predictions methods, and experimental results that compare the new methods with well-known methods in link prediction. Results show better performance for the proposed link prediction method when applied to a friendship Facebook dataset, which is characterized in this paper. The experiment is described in details and the results (precision and number of positives) shows superiority of proposed methods in terms of performance over well-known link prediction methods.
机译：本文调查社交网络（Facebook）中的链路预测方法，并讨论了两个新的链路预测方法。这两种方法是基于结构的方法，这意味着它们不是基于用户配置文件的任何内容，而是基于社交网络中的用户的连接。这两种方法是邻居和节点连接预测方法的常见邻居。第一引入方法可以被认为是对公共邻居链路预测方法的扩展。第二种方法基于邻居的平均连接。本文讨论了两种方法，并已用于实验。另外，公式，说明，伪代码和示例包括关于一些预先存在的链路预测方法和本文考虑的引入方法。本文还包括链路预测方法的详细应用，以及与链路预测中具有众所周知的方法的新方法进行比较的实验结果。结果在应用于友谊Facebook DataSet时，可以为所提出的链路预测方法表现出更好的性能，这在本文中的特征。详细描述了实验，结果（阳性精度和数量）显示了在众所周知的链路预测方法方面的性能方面的优越性。

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