聚类系数对Jaccard指数的显著性

Anand Kumar Gupta, Neetu Sardana
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引用次数: 20

摘要

链路预测是一项关键任务,通过测量网络中节点之间的接近度来识别网络中现有非连接成员之间的未来链路。基于节点邻域的链路预测技术被广泛用于预测未来的链路。这些技术可以应用于生物蛋白-蛋白相互作用网络、社会网络、信息网络和引文网络等各种应用中,预测未来的链接。每个网络都有一定的结构性质。为了预测未来链接的准确性,结构特性可能起着重要作用。目前的工作是找出网络结构特性与预测精度之间的关系。正相关意味着知道网络的结构属性将有助于我们提前知道预测的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Significance of Clustering Coefficient over Jaccard Index
Link prediction is a key task to identify the future links among existing non-connected members of a network, by measuring the proximity between nodes in a network. Node neighbourhood based link prediction techniques are immensely used for prediction of future links. These techniques can be applied on various applications like biological protein- protein interaction network, social network, information network and citation network to predict the future links. Every network has got certain structural properties. For predicting the accuracy of future links, structural properties might play an important role. Current work is an effort to find out the correlation between structural properties of network and prediction accuracy. Positive Correlation will signify that knowing the structural properties of a Network will assist us to know the prediction accuracy in advance.
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