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A potential energy and mutual information based link prediction approach for bipartite networks
Scientific Reports ( IF 4.6 ) Pub Date : 2020-11-26 , DOI: 10.1038/s41598-020-77364-9
Purushottam Kumar , Dolly Sharma

Link prediction in networks has applications in computer science, graph theory, biology, economics, etc. Link prediction is a very well studied problem. Out of all the different versions, link prediction for unipartite graphs has attracted most attention. In this work we focus on link prediction for bipartite graphs that is based on two very important concepts—potential energy and mutual information. In the three step approach; first the bipartite graph is converted into a unipartite graph with the help of a weighted projection, next the potential energy and mutual information between each node pair in the projected graph is computed. Finally, we present Potential Energy-Mutual Information based similarity metric which helps in prediction of potential links. To evaluate the performance of the proposed algorithm four similarity metrics, namely AUC, Precision, Prediction-power and Precision@K were calculated and compared with eleven baseline algorithms. The Experimental results show that the proposed method outperforms the baseline algorithms.



中文翻译:

一种基于势能和互信息的双向网络链路预测方法

网络中的链接预测已在计算机科学,图论,生物学,经济学等领域得到应用。链接预测是一个研究得非常充分的问题。在所有不同的版本中,单方图的链接预测引起了最多的关注。在这项工作中,我们专注于基于两个非常重要的概念(势能和互信息)的二部图的链接预测。在三步法中;首先在加权投影的帮助下将二部图转换为单部图,然后计算投影图中每个节点对之间的势能和互信息。最后,我们提出了基于势能-互信息的相似性度量,该度量有助于预测潜在的链接。为了评估所提出算法的性能,四个相似性指标即AUC,计算了精度,预测能力和Precision @ K,并与11种基线算法进行了比较。实验结果表明,该方法优于基线算法。

更新日期:2020-11-27
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