An Unconstrained Symmetric Nonnegative Latent Factor Analysis for Large-scale Undirected Weighted Networks

TL;DR

This paper introduces an unconstrained symmetric nonnegative latent-factor-analysis model for large-scale undirected weighted networks, improving flexibility and prediction accuracy over previous constrained models.

Contribution

It proposes a novel USNL model that separates latent factors from decision parameters and uses SGD for unconstrained training, enhancing flexibility and performance.

Findings

USNL outperforms SNL in missing data prediction accuracy

USNL achieves high computational efficiency

Empirical results on real data validate the model's effectiveness

Abstract

Large-scale undirected weighted networks are usually found in big data-related research fields. It can naturally be quantified as a symmetric high-dimensional and incomplete (SHDI) matrix for implementing big data analysis tasks. A symmetric non-negative latent-factor-analysis (SNL) model is able to efficiently extract latent factors (LFs) from an SHDI matrix. Yet it relies on a constraint-combination training scheme, which makes it lack flexibility. To address this issue, this paper proposes an unconstrained symmetric nonnegative latent-factor-analysis (USNL) model. Its main idea is two-fold: 1) The output LFs are separated from the decision parameters via integrating a nonnegative mapping function into an SNL model; and 2) Stochastic gradient descent (SGD) is adopted for implementing unconstrained model training along with ensuring the output LFs nonnegativity. Empirical studies on four SHDI matrices generated from real big data applications demonstrate that an USNL model achieves higher prediction accuracy of missing data than an SNL model, as well as highly competitive computational efficiency.