Skeleton network extraction is a crucial context in studying the core structure and essential information on complex networks. The objective of this paper is to introduce the novel network extraction method, namely, TPKS-skeleton, for investigating the global terrorism network. Our method aims to reduce the network’s size while preserving key topology and spatial features. A TPKS-skeleton comprises three steps: node evaluation, similarity-based clustering, and skeleton network reconstruction. The importance of skeleton nodes is quantified by the improved topology potential algorithm. Similarity-based clustering is then integrated to allow detecting high incident concentrations and allocating the important nodes according to the event features and spatial distribution. Finally, the skeleton network can be reconstructed by aggregating high-influential nodes from each cluster and their simplified edges. To verify the efficiency of the proposed method, we carry out three classes of a network assessment framework: node-equivalence assessment, network-equivalence assessment, and spatial information assessment. For each class, various assessment indexes were performed using the original network as a benchmark. The results verify that our proposed TPKS-skeleton outperforms other competitive methods in particular node-equivalence by Spearman rank correlation and high network structural-equivalence defined by quadratic assignment procedure. In the spatial perspective, the TPKS-skeleton network preserves reasonably all kinds of spatial information. Our study paves the way to extract the optimal skeleton of the global terrorism network, which might be beneficial for counterterrorism and network analysis in wider areas.

中文翻译：

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基于m-修正拓扑势的全球恐怖主义网络骨架提取

骨架网络提取是研究复杂网络的核心结构和基本信息的关键环境。本文的目的是介绍一种新颖的网络提取方法，即TPKS骨架，用于调查全球恐怖主义网络。我们的方法旨在在保留关键拓扑和空间特征的同时减小网络规模。TPKS骨架包括三个步骤：节点评估，基于相似性的聚类和骨架网络重构。骨架节点的重要性通过改进的拓扑势算法进行量化。然后集成基于相似度的聚类，以允许检测高入射集中度并根据事件特征和空间分布分配重要节点。最后，可以通过聚合来自每个群集及其简化边缘的高影响力节点来重建骨架网络。为了验证所提方法的有效性，我们执行了三类网络评估框架：节点等效性评估，网络等效性评估和空间信息评估。对于每个班级，使用原始网络作为基准执行了各种评估指标。结果证明，我们提出的TPKS骨架优于其他竞争方法，特别是通过Spearman秩相关和通过二次分配程序定义的高网络结构等效性实现的节点等效性。从空间角度看，TPKS骨架网络可以合理地保留各种空间信息。