Proximal Symmetric Non-negative Latent Factor Analysis: A Novel Approach to Highly-Accurate Representation of Undirected Weighted Networks
This addresses the challenge of low scalability and representation learning ability in big data applications involving undirected weighted networks, though it appears incremental as it builds on existing latent factor analysis methods.
The paper tackles the problem of representing undirected weighted networks with symmetric, high-dimensional, and incomplete matrices by proposing the Proximal Symmetric Nonnegative Latent-factor-analysis (PSNL) model, which achieves higher accuracy gain than state-of-the-art models and competitive computational efficiency in empirical studies on four networks.
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.