复杂网络中的信息核识别
Informative core identification in complex networks
摘要：
在网络分析中，建模兴趣的核心结构通常隐藏在一个更大的网络中，其中大多数结构都没有信息。网络中的非信息成分所引入的噪声和偏差会掩盖显著结构，限制许多网络建模过程的有效性。本文提出了一种新的网络非信息外围结构的核心-外围模型，而没有对信息核心结构施加特定的形式。我们提出了光谱算法的核心识别作为一般下游网络分析任务的基础上的模型的数据预处理步骤。该算法具有很强的理论保证的准确性和可扩展性的大型网络。我们评估所提出的方法进行了广泛的模拟研究，证明了各种优势，许多传统的核心外围方法。该方法被应用于从引文网络中提取信息核心结构，并在下游层次社区检测中给出更多的信息结果。
英文摘要：
In network analysis, the core structure of modeling interest is usually hidden in a larger network in which most structures are not informative. The noise and bias introduced by the non-informative component in networks can obscure the salient structure and limit many network modeling procedures' effectiveness. This paper introduces a novel core-periphery model for the non-informative periphery structure of networks without imposing a specific form for the informative core structure. We propose spectral algorithms for core identification as a data preprocessing step for general downstream network analysis tasks based on the model. The algorithm enjoys a strong theoretical guarantee of accuracy and is scalable for large networks. We evaluate the proposed method by extensive simulation studies demonstrating various advantages over many traditional core-periphery methods. The method is applied to extract the informative core structure from a citation network and give more informative results in the downstream hierarchical community detection.
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。
