TY - JOUR
T1 - A Second-Order Symmetric Non-Negative Latent Factor Model for Undirected Weighted Network Representation
AU - Li, Weiling
AU - Wang, Renfang
AU - Luo, Xin
AU - Zhou, Meng Chu
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grants 62102086 and 62272078, in part by Guangdong Province Universities and College Pearl River Scholar Funded Scheme (2019), in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2019A1515111058, in part by China Postdoctoral Science Foundation Funded Project under Grant 2020M683293, in part by CAAI-Huawei MindSpore Open Fund under Grant CAAIXSJLJJ-2021-035A, in part by the Project of the Science and Technology Plan for Zhejiang Province under Grant LGF21F020023, and in part by the Plan Project of Ningbo Municipal Science and Technology under Grants 2021Z050 and 2022S002.
Publisher Copyright:
© 2013 IEEE.
PY - 2023/3/1
Y1 - 2023/3/1
N2 - 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.
AB - 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.
KW - Conjugate gradient descent
KW - hessian-vector product
KW - latent factor analysis
KW - representation learning
KW - second-order optimization
KW - symmetric
KW - undirected weighted network
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U2 - 10.1109/TNSE.2022.3206802
DO - 10.1109/TNSE.2022.3206802
M3 - Article
AN - SCOPUS:85139444031
SN - 2327-4697
VL - 10
SP - 606
EP - 618
JO - IEEE Transactions on Network Science and Engineering
JF - IEEE Transactions on Network Science and Engineering
IS - 2
ER -