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Alternating nonnegative least squares-incorporated regularized symmetric latent factor analysis for undirected weighted networks
Zhong, Yurong1; Liu, Kechen2; Chen, Jiqiu3,4; Zhe, Xie1; Li, Weiling1
2024-11-28
摘要An Undirected Weighted Network (UWN) can be precisely quantified as an adjacency matrix whose inherent characteristics are fully considered in a Symmetric Nonnegative Latent Factor (SNLF) model for its good representation accuracy. However, an SNLF model uses a sole latent factor matrix to precisely describe the topological characteristic of a UWN, i.e., symmetry, thereby impairing its representation learning ability. Aiming at addressing this issue, this paper proposes an Alternating nonnegative least squares-incorporated Regularized Symmetric Latent factor analysis (ARSL) model. First of all, equation constraints composed of multiple matrices are built in its learning objective for well describing the symmetry of a UWN. Note that it adopts an L2 2-norm- based regularization scheme to relax such constraints for making such a symmetry-aware learning objective solvable. Then, it designs an alternating nonnegative least squares-incorporated algorithm for optimizing its parameters efficiently. Empirical studies on four UWNs demonstrate that an ARSL model outperforms the stateof-the-art models in terms of representation accuracy, as well as achieves promising computational efficiency.
关键词Undirected Weighted Network Symmetric Nonnegative Latent Factor Model Alternating Nonnegative Least Squares Missing Data Estimation
DOI10.1016/j.neucom.2024.128440
发表期刊NEUROCOMPUTING
ISSN0925-2312
卷号607页码:12
通讯作者Chen, Jiqiu(cigityrc@163.com)
收录类别SCI
WOS记录号WOS:001304251700001
语种英语