自适应利用网络多结构特征改进复杂网络链接预测
Improving link prediction in complex networks by adaptively exploiting multiple structural features of networks
摘要：
到目前为止，已经提出了许多基于网络结构的链路预测方法。然而，这些方法只突出了一个或两个网络的结构特征，然后使用该方法来预测不同网络中的缺失链接。这些现有的方法的性能并不总是令人满意的，在所有情况下，因为每个网络有其独特的底层结构特征。本文通过对不同的真实网络的分析，发现不同网络的结构特征有着显著的差异。特别是，即使在同一个网络中，它们的内部结构特征也截然不同。因此，应考虑更多的结构特征。然而，由于结构特征的显著差异，很难预先给出不同特征的贡献。受这些事实的启发，一个\emph{adaptive}融合模型的链接预测提出了将多个结构特征。该模型首先定义了一个包含多个结构特征的逻辑函数，然后利用已知的结构信息确定逻辑函数中各特征的权重\emph{adaptively}。最后，我们使用“学习”逻辑函数来预测丢失链接的连接概率。根据我们的实验结果，我们发现我们的自适应融合模型的性能优于许多相似性指标。
英文摘要：
So far, many network-structure-based link prediction methods have been proposed. However, these methods only highlight one or two structural features of networks, and then use the methods to predict missing links in different networks. The performances of these existing methods are not always satisfied in all cases since each network has its unique underlying structural features. In this paper, by analyzing different real networks, we find that the structural features of different networks are remarkably different. In particular, even in the same network, their inner structural features are utterly different. Therefore, more structural features should be considered. However, owing to the remarkably different structural features, the contributions of different features are hard to be given in advance. Inspired by these facts, an \emph{adaptive} fusion model regarding link prediction is proposed to incorporate multiple structural features. In the model, a logistic function combing multiple structural features is defined, then the weight of each feature in the logistic function is \emph{adaptively} determined by exploiting the known structure information. Last, we use the "learnt" logistic function to predict the connection probabilities of missing links. According to our experimental results, we find that the performance of our adaptive fusion model is better than many similarity indices.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
