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An Undirected Weighted Network (UWN) is commonly found in big data-relatedapplications. 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 intrinsicsymmetry or low-data density, resulting in low model scalability orrepresentation learning ability. For addressing this issue, a ProximalSymmetric Nonnegative Latent-factor-analysis (PSNL) model is proposed. Itincorporates a proximal term into symmetry-aware and data density-orientedobjective function for high representation accuracy. Then an adaptiveAlternating Direction Method of Multipliers (ADMM)-based learning scheme isimplemented through a Tree-structured of Parzen Estimators (TPE) method forhigh computational efficiency. Empirical studies on four UWNs demonstrate thatPSNL achieves higher accuracy gain than state-of-the-art models, as well ashighly competitive computational efficiency.

Proximal Symmetric Non-negative Latent Factor Analysis: A Novel Approach to Highly-Accurate Representation of Undirected Weighted Networks