Title: Local clustering coefficient based on three-way partial correlations in climate networks as a new marker of tropical cyclone Show Chinese title

Title: 基于气候网络中三向部分相关性的局部聚类系数作为热带气旋的新标记

Abstract: We introduce a new network marker for climate network analysis. It is based upon an available special definition of local clustering coefficient for weighted correlation networks, which was previously introduced in the neuroscience context and aimed at compensating for uninformative correlations caused by indirect interactions. We modify this definition further by replacing Pearson's pairwise correlation coefficients and Pearson's three-way partial correlation coefficients by the respective Kendall's rank correlations. This reduces statistical sample size requirements to compute the correlations, which translates into the possibility of using shorter time windows and hence into shorter response time of the real-time climate network analysis. We compare this proposed network marker to the conventional local clustering coefficient based on unweighted networks obtained by thresholding the correlation matrix. We show several examples where the new marker is found to be better associated to tropical cyclones than the unweighted local clustering coefficient. Show Chinese abstract

Abstract: 我们引入了一种新的网络标记用于气候网络分析。 它是基于加权相关网络的一个可用的局部聚类系数的特殊定义，该定义之前在神经科学背景下引入，旨在补偿由间接相互作用引起的无信息相关性。 我们通过将皮尔逊的成对相关系数和皮尔逊的三向部分相关系数替换为相应的肯德尔等级相关系数，进一步修改了这一定义。 这降低了计算相关性的统计样本量要求，从而转化为使用较短的时间窗口的可能性，进而转化为实时气候网络分析的更短响应时间。 我们将这种提出的网络标记与通过阈值处理相关矩阵获得的基于无权重网络的常规局部聚类系数进行比较。 我们展示了几个例子，其中新标记被发现比无权重局部聚类系数更紧密地与热带气旋相关。