用于测量节点影响的可校准Hetero-NodeRank
Calibratable Hetero-NodeRank for measuring node influence
摘要：
节点影响力度量已经被应用到许多应用中，包括互联网上的网页排名，或空间网络上的位置。PageRank是一种流行且有效的估计节点影响力的算法。然而，传统的PageRank方法既没有考虑网络结构的异质性，也没有考虑网络的附加信息，这对性能的提高造成了很大的阻碍，并且低估了非中心节点的重要性。由于对这些问题的研究还很有限，现有的解决方案还不能令人满意。本文提出了一个通用的PageRank为基础的模型框架，被称为异构NodeRank，占异构网络拓扑结构，并结合节点属性信息，以捕捉两个链接和基于节点的影响，在测量节点的影响力的问题。此外，该框架使校准所提出的模型对经验数据，这将原来的演绎方法转化为一个归纳的，可能是有用的假设检验和因果关系分析。由于原始的无监督任务变成了有监督任务，因此可以利用优化方法进行模型校准。在中国城市网的实际数据上的实验表明，该模型优于几种广泛使用的算法。
英文摘要：
Node influence metrics have been applied to many applications, including ranking web pages on internet, or locations on spatial networks. PageRank is a popular and effective algorithm for estimating node influence. However, conventional PageRank method considers neither the heterogeneity of network structures nor additional network information, causing a major impedance to performance improvement and an underestimation of non-hub nodes' importance. As these problems are only partially studied, existing solutions are still not satisfying. This paper addresses the problems by presenting a general PageRank-based model framework, dubbed Hetero-NodeRank, that accounts for heterogeneous network topology and incorporates node attribute information to capture both link- and node-based effects in measuring node influence. Moreover, the framework enables the calibration of the proposed model against empirical data, which transforms the original deductive approach into an inductive one that could be useful for hypothesis testing and causal-effect analysis. As the original unsupervised task becomes a supervised one, optimization methods can be leveraged for model calibrations. Experiments on real data from the national city network of China demonstrated that the proposed model outperforms several widely used algorithms.
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。
