Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 34, Issue 8, Pages 4826-4840Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2021.3125774
Keywords
Symmetric matrices; Computational modeling; Data models; Analytical models; Training; Learning systems; Convergence; Alternating-direction-method of multipliers (ADMM); learning system; missing data; non-negative latent factor analysis (NLFA); symmetric high-dimensional and incomplete matrix (SHDI); undirected weighted network
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This study proposes an ASNL model for handling large-scale undirected networks, which can efficiently represent incomplete and imbalanced data of SHDI matrices, and has fast model convergence and high computational efficiency. Empirical studies on four SHDI matrices demonstrate that ASNL significantly outperforms other models in prediction accuracy and computational efficiency.
Large-scale undirected weighted networks are frequently encountered in big-data-related applications concerning interactions among a large unique set of entities. Such a network can be described by a Symmetric, High-Dimensional, and Incomplete (SHDI) matrix whose symmetry and incompleteness should be addressed with care. However, existing models fail in either correctly representing its symmetry or efficiently handling its incomplete data. For addressing this critical issue, this study proposes an Alternating-Direction-Method of Multipliers (ADMM)-based Symmetric Non-negative Latent Factor Analysis (ASNL) model. It adopts fourfold ideas: 1) implementing the data density-oriented modeling for efficiently representing an SHDI matrix's incomplete and imbalanced data; 2) separating the non-negative constraints from the decision parameters to avoid truncations during the training process; 3) incorporating the ADMM principle into its learning scheme for fast model convergence; and 4) parallelizing the training process with load balance considerations for high efficiency. Empirical studies on four SHDI matrices demonstrate that ASNL significantly outperforms several state-of-the-art models in both prediction accuracy for missing data of an SHDI and computational efficiency. It is a promising model for handling large-scale undirected networks raised in real applications.
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