The clustering coefficient is a valuable tool for understanding the structure of complex networks. It is widely used to analyze social networks, biological networks, and other complex systems. While there is generally a single common definition for the local clustering coefficient, there are two different ways to calculate the global clustering coefficient. The first approach takes the average of the local clustering coefficients for each node in the network. The second one is based on the ratio of closed triplets to all triplets. It is shown that these two definitions of the global clustering coefficients are strongly inequivalent and may significantly impact the accuracy of the outcome.

翻译：聚集系数是理解复杂网络结构的重要工具，已被广泛应用于社会网络、生物网络及其他复杂系统的分析。尽管局部聚集系数通常具有单一公认的定义，但全局聚集系数的计算存在两种不同方法：第一种方法取网络中所有节点局部聚集系数的平均值；第二种方法基于闭合三元组与全部三元组的比值。研究表明，这两种全局聚集系数的定义存在显著不等价性，可能对分析结果的准确性产生重要影响。