As an elementary task in statistical physics and network science, link prediction has attracted great attention of researchers from many fields. While numerous similarity-based indices have been designed for undirected networks, link prediction in directed networks has not been thoroughly studied yet. Among several representative works, motif predictors such as “feed-forward-loop” and Bi-fan predictor perform well in both accuracy and efficiency. Nevertheless, they fail to explicitly explain the linkage motivation of nodes, nor do they consider the unequal contributions of different neighbors between node pairs. In this paper, motivated by the investment theory in economics, we propose a universal and explicable model to quantify the contributions of nodes on driving link formation. Based on the analysis on two typical investment relationships, namely “follow-up” and “co-follow”, an investment-profit index is designed for link prediction in directed networks. Empirical studies on 12 static networks and four temporal networks show that the proposed method outperforms eight mainstream baselines under three standard metrics. As a quasi-local index, it is also suitable for large-scale networks.

中文翻译：

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基于局部网络结构和投资理论的有向网络缺失链路预测

作为统计物理学和网络科学中的一项基本任务，链路预测已经引起了许多领域的研究人员的极大关注。虽然已经为无向网络设计了许多基于相似性的索引，但有向网络中的链接预测尚未得到彻底研究。在几个具有代表性的作品中，诸如“前馈循环”和 Bi-fan 预测器等主题预测器在准确性和效率上都表现良好。然而，他们未能明确解释节点的链接动机，也没有考虑节点对之间不同邻居的不平等贡献。在本文中，受经济学投资理论的启发，我们提出了一个通用且可解释的模型来量化节点对驱动链接形成的贡献。基于对两种典型投资关系的分析，即“follow-up”和“co-follow”，为有向网络中的链路预测设计了一个投资收益指数。对 12 个静态网络和 4 个时间网络的实证研究表明，所提出的方法在三个标准指标下优于 8 个主流基线。作为准局部索引，它也适用于大规模网络。