An Undirected Weighted Network (UWN) is commonly found in big data-related applications. Note that such a network's information connected with its nodes, and edges can be expressed as a Symmetric, High-Dimensional and Incomplete (SHDI) matrix. However, existing models fail in either modeling its intrinsic symmetry or low-data density, resulting in low model scalability or representation learning ability. For addressing this issue, a Proximal Symmetric Nonnegative Latent-factor-analysis (PSNL) model is proposed. It incorporates a proximal term into symmetry-aware and data density-oriented objective function for high representation accuracy. Then an adaptive Alternating Direction Method of Multipliers (ADMM)-based learning scheme is implemented through a Tree-structured of Parzen Estimators (TPE) method for high computational efficiency. Empirical studies on four UWNs demonstrate that PSNL achieves higher accuracy gain than state-of-the-art models, as well as highly competitive computational efficiency.

翻译：无向加权网络（UWN）常见于大数据相关应用中。此类网络与节点及其边相关的信息可表示为对称、高维且不完整（SHDI）矩阵。然而，现有模型无法同时建模其内在对称性或低数据密度，导致模型可扩展性或表示学习能力低下。为解决此问题，提出一种近端对称非负潜在因子分析（PSNL）模型。该模型在对称感知和面向数据密度的目标函数中引入近端项，以实现高表示精度。随后通过基于树结构帕岑估计器（TPE）方法实现自适应交替方向乘子法（ADMM）学习方案，以提升计算效率。对四个UWN的实验研究表明，PSNL在准确率增益上优于最先进模型，并具有高度竞争力的计算效率。