有向网络的势理论
Potential Theory for Directed Networks
摘要：
揭示网络形成的潜在因素是数据挖掘和网络分析的一个长期挑战。特别是，有向网络的微观组织原理比无向网络的微观组织原理更不容易理解。本文提出了一个势理论假设，即每个有向连接对应于一个单位势的减少，并且所有节点的势值都可定义的子图是优选的。结合势函数理论、聚类机制和同伦机制，推导出由4个节点和4个有向链路组成的Bi-fan结构是有向网络中最受青睐的局部结构。我们的假设得到了来自不同领域的15个有向网络的广泛实验的强烈支持，如链接预测框架内的Bi-fan预测器的最准确和最强大的性能所示。综上所述，本文的主要贡献有两个方面：（i）提出了一种新的有向网络局部组织机制;（ii）设计了相应的链接预测算法，不仅验证了我们的假设，而且在缺失链接预测和友谊推荐中找到了直接的应用。
英文摘要：
Uncovering factors underlying the network formation is a long-standing challenge for data mining and network analysis. In particular, the microscopic organizing principles of directed networks are less understood than those of undirected networks. This article proposes a hypothesis named potential theory, which assumes that every directed link corresponds to a decrease of a unit potential and subgraphs with definable potential values for all nodes are preferred. Combining the potential theory with the clustering and homophily mechanisms, it is deduced that the Bi-fan structure consisting of 4 nodes and 4 directed links is the most favored local structure in directed networks. Our hypothesis receives strongly positive supports from extensive experiments on 15 directed networks drawn from disparate fields, as indicated by the most accurate and robust performance of Bi-fan predictor within the link prediction framework. In summary, our main contribution is twofold: (i) We propose a new mechanism for the local organization of directed networks; (ii) We design the corresponding link prediction algorithm, which can not only testify our hypothesis, but also find out direct applications in missing link prediction and friendship recommendation.
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。
