Community detection is a challenging issue because most existing methods are not well suited for complex social networks with ambiguous structures. In this paper, we propose a novel community detection method named Stacked Autoencoder-Based Community Detection Method via Ensemble Clustering (CDMEC). This is the first time that we have attempted to apply four different complex network similarity representations to the community detection problem. This work makes up for the insufficiency of the single similarity matrix to describe the similarity relationship between nodes. These similarity representations can fully describe and consider the sufficient local information between nodes in a network topology. Our CDMEC framework combines transfer learning and a stacked autoencoder to obtain an efficient low-dimensional feature representation of complex networks and aggregates multiple inputs through a novel ensemble clustering framework. This novel framework first uses the basic clustering results to construct a consistent matrix, and then it employs the nonnegative matrix factorization (NMF)-based clustering method to detect reliable clustering results from the consistent matrix. The results of various extensive experiments on artificial benchmark networks and real-world networks showed that the proposed CDMEC framework is superior to the existing state-of-the-art community detection methods and has great potential in solving the community detection problems.

社区检测是一个具有挑战性的问题，因为大多数现有方法不适用于结构模糊的复杂社交网络。在本文中，我们提出了一种新颖的社区检测方法，即通过集成聚类（CDMEC）的基于堆栈自动编码器的社区检测方法。这是我们第一次尝试将四种不同的复杂网络相似性表示形式应用于社区检测问题。这项工作弥补了单个相似度矩阵不足以描述节点之间的相似度关系的不足。这些相似性表示可以完全描述和考虑网络拓扑中节点之间的足够本地信息。我们的CDMEC框架结合了转移学习和堆叠式自动编码器，从而获得了复杂网络的高效低维特征表示，并通过新颖的集成聚类框架汇总了多个输入。这个新颖的框架首先使用基本的聚类结果来构造一个一致的矩阵，然后使用基于非负矩阵分解（NMF）的聚类方法从一致的矩阵中检测出可靠的聚类结果。在人工基准网络和现实网络上进行的各种广泛实验的结果表明，所提出的CDMEC框架优于现有的最新社区检测方法，并且在解决社区检测问题方面具有巨大潜力。这个新颖的框架首先使用基本的聚类结果来构造一个一致的矩阵，然后使用基于非负矩阵分解（NMF）的聚类方法从一致的矩阵中检测出可靠的聚类结果。在人工基准网络和现实网络上进行的各种广泛实验的结果表明，所提出的CDMEC框架优于现有的最新社区检测方法，并且在解决社区检测问题方面具有巨大潜力。这个新颖的框架首先使用基本的聚类结果来构造一个一致的矩阵，然后使用基于非负矩阵分解（NMF）的聚类方法从一致的矩阵中检测出可靠的聚类结果。在人工基准网络和现实网络上进行的各种广泛实验的结果表明，所提出的CDMEC框架优于现有的最新社区检测方法，并且在解决社区检测问题方面具有巨大潜力。