Data from: Matching-centrality decomposition and the forecasting of new links in networks

Networks play a prominent role in the study of complex systems of interacting entities in biology, sociology, and economics. Despite this diversity, we demonstrate here that a statistical model decomposing networks into matching and centrality components provides a comprehensive and unifying quantification of their architecture. The matching term quantifies the assortative structure in which node makes links with which other node, while the centrality term quantifies the number of links that nodes make. We show, for a diverse set of networks, that this decomposition can provide a tight fit to observed networks. Then we provide three applications. First, we show that the model allows very accurate prediction of missing links in partially known networks. Second, when node characteris- tics are known, we show how the matching-centrality decomposition can be related to this external information. Consequently, it offers a simple and versatile tool to explore how node characteristics ex- plain network architecture. Finally, we demonstrate the efficiency and flexibility of the model to forecast the links that a novel node would create if it were to join an existing network.

网络在生物学、社会学及经济学领域中，针对交互实体构成的复杂系统研究占据重要地位。尽管研究涉及的网络类型多样，本文证明，一种将网络分解为匹配(matching)与中心性(centrality)分量的统计模型，可对网络架构实现全面且统一的量化表征。其中匹配项用于量化节点的连边选择模式，即同配性(assortative)结构——节点与哪些其他节点建立连边；而中心性项则用于量化节点的连边总数量。针对多样化的网络集合，本文证明该分解方法可与实测网络实现高度拟合。随后本文给出三项应用：其一，该模型可精准预测部分已知网络中的缺失连边；其二，当节点特征已知时，本文展示了如何将匹配-中心性分解方法与该外部信息建立关联，进而为探究节点特征如何解释网络架构提供一种简洁且通用的分析工具；其三，本文验证了该模型在预测新增节点接入现有网络时所能建立的连边方面，具备高效性与灵活性。