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SSDBA: the stretch shrink distance based algorithm for link prediction in social networks
Frontiers of Computer Science ( IF 4.2 ) Pub Date : 2020-08-13 , DOI: 10.1007/s11704-019-9083-3
Ruidong Yan , Yi Li , Deying Li , Weili Wu , Yongcai Wang

In the field of social network analysis, Link Prediction is one of the hottest topics which has been attracted attentions in academia and industry. So far, literatures for solving link prediction can be roughly divided into two categories: similarity-based and learning-based methods. The learning-based methods have higher accuracy, but their time complexities are too high for complex networks. However, the similarity-based methods have the advantage of low time consumption, so improving their accuracy becomes a key issue. In this paper, we employ community structures of social networks to improve the prediction accuracy and propose the stretch shrink distance based algorithm (SSDBA). In SSDBA, we first detect communities of a social network and identify active nodes based on community average threshold (CAT) and node average threshold (NAT) in each community. Second, we propose the stretch shrink distance (SSD) model to iteratively calculate the changes of distances between active nodes and their local neighbors. Finally, we make predictions when these links’ distances tend to converge. Furthermore, extensive parameters learning have been carried out in experiments. We compare our SSDBA with other popular approaches. Experimental results validate the effectiveness and efficiency of proposed algorithm.

中文翻译:

SSDBA:基于拉伸收缩距离的社交网络链接预测算法

在社交网络分析领域,链接预测是最热门的话题之一,在学术界和工业界都引起了人们的关注。到目前为止,用于解决链接预测的文献大致可以分为两类:基于相似性的方法和基于学习的方法。基于学习的方法具有较高的准确性,但是它们的时间复杂度对于复杂的网络而言太高了。然而,基于相似度的方法具有耗时少的优点,因此提高其准确性成为关键问题。在本文中,我们利用社交网络的社区结构来提高预测准确性,并提出了基于伸缩距离的算法(SSDBA)。在SSDBA中,我们首先检测社交网络的社区并根据以下信息确定活动节点每个社区中的社区平均阈值(CAT)和节点平均阈值(NAT)。其次,我们提出了拉伸收缩距离(SSD)模型,以迭代方式计算活动节点与其本地邻居之间的距离变化。最后,当这些链接的距离趋于收敛时,我们进行预测。此外,已经在实验中进行了广泛的参数学习。我们将SSDBA与其他流行方法进行了比较。实验结果验证了所提算法的有效性和有效性。
更新日期:2020-08-13
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