A blockmodel is a network in which the nodes are clusters of equivalent (in terms of the structure of the links connecting) nodes in the network being studied. The term block refers to the links between two clusters. When structural equivalence is relied on, two types of blocks are possible: complete blocks and null blocks. Ideally, all possible links are found in complete blocks while there are no links in null blocks. Yet, in the case of empirical networks, some links frequently appear in null blocks and some non-links appear in complete blocks. These links and non-links are called inconsistencies. When a relocation algorithm is applied to obtain a blockmodel, the criterion function is minimised. The number of inconsistencies is reflected in a criterion function’s value, leading to it being regularly used to fit an empirical network to an ideal blockmodel. Since the value of a criterion function depends on various factors (e.g. the block types allowed, the network size and its density), the values obtained for different networks are incomparable. To address this deficiency, the Relative Fit measure is proposed in this paper. Relative Fit values may be used to select the appropriate blockmodel type and/or number of clusters. Values of the Relative Fit measure can also be of value when fitting different empirical networks to a given blockmodel.

块模型是一种网络，其中节点是正在研究的网络中的等效（就连接链接的结构而言）节点的集群。术语块是指两个集群之间的链接。当依赖结构等价时，可能有两种类型的块：完整块和空块。理想情况下，所有可能的链接都在完整块中找到，而空块中没有链接。然而，在经验网络的情况下，一些链接经常出现在空块中，而一些非链接出现在完整块中。这些链接和非链接被称为不一致。当应用重定位算法来获得块模型时，准则函数被最小化。不一致的数量反映在标准函数的值中，导致它经常用于将经验网络拟合到理想的块模型。由于准则函数的值取决于各种因素（例如允许的块类型、网络大小及其密度），因此不同网络获得的值是不可比的。为了解决这个不足，本文提出了相对拟合度量。相对拟合值可用于选择适当的块模型类型和/或聚类数。当将不同的经验网络拟合到给定的块模型时，Relative Fit 度量的值也很有价值。本文提出了相对拟合度量。相对拟合值可用于选择适当的块模型类型和/或聚类数。当将不同的经验网络拟合到给定的块模型时，Relative Fit 度量的值也很有价值。本文提出了相对拟合度量。相对拟合值可用于选择适当的块模型类型和/或聚类数。当将不同的经验网络拟合到给定的块模型时，Relative Fit 度量的值也很有价值。