标题： 监督块建模 显示英文标题

标题： Supervised Blockmodelling

摘要： 集体分类模型试图通过考虑相关实例的类别标签来提高分类性能。 然而，它们往往不学习类之间的交互模式，或者做出实例属于同一类别的假设（同质性假设）。 块模型为解决这些问题提供了一种方法，能够对同质性和异质性交互进行建模，并以汇总网络的形式学习交互模式。 监督块模型仅使用链接结构就能提供良好的分类性能，同时提供可解释的网络交互汇总，以帮助更好地理解数据。 这项工作探讨了三种不同复杂度的监督块模型，并在四个结构不同的现实世界网络上进行了测试。 显示英文摘要

摘要： Collective classification models attempt to improve classification performance by taking into account the class labels of related instances. However, they tend not to learn patterns of interactions between classes and/or make the assumption that instances of the same class link to each other (assortativity assumption). Blockmodels provide a solution to these issues, being capable of modelling assortative and disassortative interactions, and learning the pattern of interactions in the form of a summary network. The Supervised Blockmodel provides good classification performance using link structure alone, whilst simultaneously providing an interpretable summary of network interactions to allow a better understanding of the data. This work explores three variants of supervised blockmodels of varying complexity and tests them on four structurally different real world networks.