A Second-Order Symmetric Non-Negative Latent Factor Model for Undirected Weighted Network Representation

Document Type

Article

Publication Date

3-1-2023

Abstract

Precise representation to undirected weighted network (UWN) is the foundation of understanding connection patterns inside a massive node set. It can be addressed via a Symmetric Non-negative Latent Factor (SNLF) model with a non-convex learning objective. However, existing SNLF models commonly adopt a first-order learning algorithm that cannot well handle such a non-convex objective, thereby leading to inaccurate UWN representation. Aiming at addressing this issue, this study incorporates an efficient second-order learning algorithm into an SNLF model, thereby establishing a Second-order Symmetric Non-negative Latent Factor (S2NLF) model with two-fold ideas: a) applying the single latent factor-related mapping function to the non-negativity constrained optimization parameters to achieve an unconstrained learning objective, and b) optimizing this learning objective with its optimization parameters through an efficient second-order learning algorithm to achieve accurate representation to the target UWN with affordable computational burden. Empirical studies indicate that owing to its efficient incorporation of the second-order optimization technique, the proposed S2NLF model outperforms state-of-the-art SNLF models when they are used to gain highly accurate representation to UWNs emerging from real applications.

Identifier

85139444031 (Scopus)

Publication Title

IEEE Transactions on Network Science and Engineering

External Full Text Location

https://doi.org/10.1109/TNSE.2022.3206802

e-ISSN

23274697

First Page

606

Last Page

618

Issue

2

Volume

10

Grant

CAAIXSJLJJ-2021-035A

Fund Ref

National Natural Science Foundation of China

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