He Tiantian, Bai Lu, Ong Yew-Soon
IEEE Trans Cybern. 2022 Aug;52(8):8047-8060. doi: 10.1109/TCYB.2021.3051606. Epub 2022 Jul 19.
In this article, we present a novel matrix-factorization-based model, labeled here as Vicinal vertex allocated matrix factorization (VVAMo), for uncovering clusters in network data. Different from the past related efforts of network clustering, which consider the edge structure, vertex features, or both in their design, the proposed model includes the additional detail on vertex inclinations with respect to topology and features into the learning. In particular, by taking the latent preferences between vicinal vertices into consideration, VVAMo is then able to uncover network clusters composed of proximal vertices that share analogous inclinations, and correspondingly high structural and feature correlations. To ensure such clusters are effectively uncovered, we propose a unified likelihood function for VVAMo and derive an alternating algorithm for optimizing the proposed function. Subsequently, we provide the theoretical analysis of VVAMo, including the convergence proof and computational complexity analysis. To investigate the effectiveness of the proposed model, a comprehensive empirical study of VVAMo is conducted using extensive commonly used realistic network datasets. The results obtained show that VVAMo attained superior performances over existing classical and state-of-the-art approaches.
在本文中，我们提出了一种基于矩阵分解的新型模型，在此标记为邻域顶点分配矩阵分解（VVAMo），用于揭示网络数据中的聚类。与过去网络聚类的相关工作不同，过去的工作在设计中考虑边结构、顶点特征或两者，而所提出的模型在学习中纳入了关于顶点相对于拓扑和特征的倾向的额外细节。特别是，通过考虑邻域顶点之间的潜在偏好，VVAMo能够揭示由具有相似倾向以及相应高结构和特征相关性的近端顶点组成的网络聚类。为确保有效揭示此类聚类，我们为VVAMo提出了一个统一的似然函数，并推导了一种用于优化该函数的交替算法。随后，我们提供了VVAMo的理论分析，包括收敛性证明和计算复杂度分析。为研究所提出模型的有效性，使用广泛的常用现实网络数据集对VVAMo进行了全面的实证研究。所得结果表明，VVAMo比现有的经典方法和最新方法具有更优的性能。
