Link prediction in temporal networks has always been a hot topic in both statistical physics and network science. Most existing works fail to consider the inner relationship between nodes, leading to poor prediction accuracy. Even though a wide range of realistic networks are temporal ones, few existing works investigated the properties of realistic and temporal networks. In this paper, we address the problem of abstracting individual attributes and propose a adaptive link prediction method for temporal networks based on H-index to predict future links. The matching degree of nodes is first defined considering both the native influence and the secondary influence of local structure. Then a similarity index is designed using a decaying parameter to punish the snapshots with their occurring time. Experimental results on five realistic temporal networks observing consistent gains of 2–9% AUC in comparison to the best baseline in four networks show that our proposed method outperforms several benchmarks under two standard evaluation metrics: AUC and Ranking score. We also investigate the influence of the free parameter and the definition of matching degree on the prediction performance.

中文翻译：

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基于节点影响的时间网络中未来链路预测新方法

时间网络中的链接预测一直是统计物理学和网络科学的热门话题。现有的大部分工作都没有考虑节点之间的内在关系，导致预测精度差。尽管广泛的现实网络是时间网络，但很少有现有的工作研究现实和时间网络的属性。在本文中，我们解决了抽象单个属性的问题，并提出了一种基于H-index 来预测未来的链接。首先定义节点的匹配度，同时考虑本地结构的原生影响和次要影响。然后使用衰减参数设计相似性指数来惩罚快照的发生时间。与四个网络中的最佳基线相比，在五个现实时间网络上观察到 2-9% AUC 的一致增益的实验结果表明，我们提出的方法在两个标准评估指标下优于几个基准：AUC 和排名分数。我们还研究了自由参数和匹配度的定义对预测性能的影响。