Beyond ranking nodes: Predicting epidemic outbreak sizes by network centralities
Beyond ranking nodes: Predicting epidemic outbreak sizes by network centralities
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DOI：
10.1371/journal.pcbi.1008052
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发表时间：
2020-07-01
影响因子：
4.3
通讯作者：
Holme, Petter
中科院分区：
文献类型：
--
作者：
Bucur, Doina;Holme, Petter
识别疾病传播的重要节点是网络流行病学的中心议题。我们调查如何以及一个节点的位置，其特征在于标准的网络措施，可以预测其流行病学的重要性，在任何图形中的一个给定的节点数量。这是在其他研究中，处理更容易预测的问题，排名节点的流行病的重要性，在给定的图形。作为流行病重要性的基准，我们计算给定节点作为源的确切预期爆发规模。我们详尽地研究了给定大小的所有图，所以不要将自己局限于某些图的生成模型，也不要局限于图数据集。由于大量的可能的非同构图的一个固定的大小，我们仅限于十节点图。我们发现，两个或两个以上的中心性的组合是预测（R-2得分为0.91或更高），即使是最困难的参数值的流行病模拟。通常，这些成功的组合包括一个归一化的谱中心性（如PageRank或Katz中心性）和一个对图中边的数量敏感的度量。
Identifying important nodes for disease spreading is a central topic in network epidemiology. We investigate how well the position of a node, characterized by standard network measures, can predict its epidemiological importance in any graph of a given number of nodes. This is in contrast to other studies that deal with the easier prediction problem of ranking nodes by their epidemic importance in given graphs. As a benchmark for epidemic importance, we calculate the exact expected outbreak size given a node as the source. We study exhaustively all graphs of a given size, so do not restrict ourselves to certain generative models for graphs, nor to graph data sets. Due to the large number of possible nonisomorphic graphs of a fixed size, we are limited to ten-node graphs. We find that combinations of two or more centralities are predictive (R-2 scores of 0.91 or higher) even for the most difficult parameter values of the epidemic simulation. Typically, these successful combinations include one normalized spectral centrality (such as PageRank or Katz centrality) and one measure that is sensitive to the number of edges in the graph.