将网络全局分解为由局部枢纽形成的多个核心
Global decomposition of networks into multiple cores formed by local hubs
AI总结：
本文提出基于局部枢纽中心性与边修剪的网络分解方法，通过迭代移除局部弱连接节点揭示洋葱状层级和多核心—边缘结构，并结合社群检测实现超节点网络分解。
摘要：
网络在各个领域中无处不在，它们表示由节点及其互连构成复杂结构的系统。我们引入一种网络分解方案，利用局部定义的节点枢纽中心性概念以及建立在其基础上的边修剪技术，来揭示潜藏在内部的多尺度核心—边缘结构。我们证明，基于枢纽中心性的边修剪揭示了网络分解中的一系列断裂点，从而有效地将网络分离为其骨干和外壳结构。我们的局部边分解方法迭代地识别并移除局部连接最少的节点，最终揭示出一种类似洋葱的层级结构。与传统的 $k$-core 分解方法相比，我们这种基于局部结构中相对信息的方法在发现局部关键子结构方面表现出明显优势。作为该方法的一项应用，我们通过将其与网络社群检测相结合，提出了一种检测多个核心—边缘结构以及分解粗粒化超节点网络的方案。
英文摘要：
Networks are ubiquitous in various fields, representing systems where nodes and their interconnections constitute their intricate structures. We introduce a network decomposition scheme to reveal multiscale core-periphery structures lurking inside, using the concept of locally defined nodal hub centrality and edge-pruning techniques built upon it. We demonstrate that the hub-centrality-based edge pruning reveals a series of breaking points in network decomposition, which effectively separates a network into its backbone and shell structures. Our local-edge decomposition method iteratively identifies and removes locally least connected nodes, and uncovers an onion-like hierarchical structure as a result. Compared with the conventional $k$-core decomposition method, our method based on relative information residing in local structures exhibits a clear advantage in terms of discovering locally crucial substructures. As an application of the method, we present a scheme to detect multiple core-periphery structures and the decomposition of coarse-grained supernode networks, by combining the method with the network 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。
