A complex network is a network with non-trivial topological structures. It contains not just topological information but also attribute information available in the rich content of nodes. Concerning the task of cluster analysis in a complex network, model-based algorithms are preferred over distance-based ones, as they avoid designing specific distance measures. However, their models are only applicable to complex networks where the attribute information is composed of attributes in binary form. To overcome this disadvantage, we introduce a three-layer node-attribute-value hierarchical structure to describe the attribute information in a flexible and interpretable manner. Then, a new Bayesian model is proposed to simulate the generative process of a complex network. In this model, the attribute information is generated by following the hierarchical structure while the links between pairwise nodes are generated by a stochastic blockmodel. To solve the corresponding inference problem, we develop a variational Bayesian algorithm called TARA, which allows us to identify functionally meaningful clusters through an iterative procedure. Our extensive experiment results show that TARA can be an effective algorithm for cluster analysis in a complex network. Moreover, the parallelized version of TARA makes it possible to perform efficiently at its tasks when applied to large complex networks.

中文翻译：

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复杂网络中聚类分析的变分贝叶斯框架

复杂网络是具有非平凡拓扑结构的网络。它不仅包含拓扑信息，还包含节点丰富内容中可用的属性信息。关于复杂网络中的聚类分析任务，基于模型的算法优于基于距离的算法，因为它们避免设计特定的距离度量。然而，他们的模型仅适用于属性信息由二进制形式的属性组成的复杂网络。为了克服这个缺点，我们引入了三层节点-属性-值层次结构，以灵活和可解释的方式描述属性信息。然后，提出了一种新的贝叶斯模型来模拟复杂网络的生成过程。在这个模型中，属性信息是按照层次结构生成的，而成对节点之间的链接是由随机块模型生成的。为了解决相应的推理问题，我们开发了一种称为 TARA 的变分贝叶斯算法，它允许我们通过迭代过程识别功能上有意义的集群。我们广泛的实验结果表明，TARA 是一种在复杂网络中进行聚类分析的有效算法。此外，当应用于大型复杂网络时，TARA 的并行化版本可以有效地执行其任务。这使我们能够通过迭代过程识别功能上有意义的集群。我们广泛的实验结果表明，TARA 是一种在复杂网络中进行聚类分析的有效算法。此外，当应用于大型复杂网络时，TARA 的并行化版本可以有效地执行其任务。这使我们能够通过迭代过程识别功能上有意义的集群。我们广泛的实验结果表明，TARA 是一种在复杂网络中进行聚类分析的有效算法。此外，当应用于大型复杂网络时，TARA 的并行化版本可以有效地执行其任务。