Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
Sci Rep. 2017 Mar 27;7:44981. doi: 10.1038/srep44981.
Recently, a number of meta-path based similarity indices like PathSim, HeteSim, and random walk have been proposed for link prediction in heterogeneous complex networks. However, these indices suffer from two major drawbacks. Firstly, they are primarily dependent on the connectivity degrees of node pairs without considering the further information provided by the given meta-path. Secondly, most of them are required to use a single and usually symmetric meta-path in advance. Hence, employing a set of different meta-paths is not straightforward. To tackle with these problems, we propose a mutual information model for link prediction in heterogeneous complex networks. The proposed model, called as Meta-path based Mutual Information Index (MMI), introduces meta-path based link entropy to estimate the link likelihood and could be carried on a set of available meta-paths. This estimation measures the amount of information through the paths instead of measuring the amount of connectivity between the node pairs. The experimental results on a Bibliography network show that the MMI obtains high prediction accuracy compared with other popular similarity indices.
最近，已经提出了许多基于元路径的相似性指标，如 PathSim、HeteSim 和随机游走，用于异构复杂网络中的链路预测。然而，这些指标存在两个主要缺点。首先，它们主要依赖于节点对的连接度，而不考虑给定元路径提供的进一步信息。其次，它们中的大多数都需要事先使用单个且通常是对称的元路径。因此，使用一组不同的元路径并不直接。为了解决这些问题，我们提出了一种用于异构复杂网络中链路预测的互信息模型。所提出的模型称为基于元路径的互信息指数（MMI），它引入了基于元路径的链接熵来估计链接可能性，并可以在一组可用的元路径上进行。这种估计通过路径测量信息量，而不是测量节点对之间的连接度。在一个 Bibliography 网络上的实验结果表明，与其他流行的相似性指标相比，MMI 获得了更高的预测精度。
