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.

本文提出了一种名为 PSNL 的模型，它通过在面向数据密度和对称性的目标函数中加入接近项来提高表示准确性，并通过基于 ADMM 的自适应 TPE 方法实现高效的计算学习方案，从而实现了四个 UWN 上的模拟研究，证明了 PSNL 比最先进的模型实现更高的准确性提高，同时具有高度竞争的计算效率。