.png){kind=icon}
Return
Network Embedding Using Semi-Supervised Kernel Nonnegative Matrix Factorization
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2019.2927496.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Network embedding, aiming to learn low-dimensional representations of nodes in networks, is very useful for many vector-based machine learning algorithms and has become a hot research topic in network analysis. Although many methods for network embedding have been proposed before, most of them are unsupervised, which ignores the role of prior information available in the network. In this paper, we propose a novel method for network embedding using semi-supervised kernel nonnegative matrix factorization (SSKNMF), which can incorporate prior information and thus to learn more useful features from the network through introducing kernel methodology. Besides, it can improve robustness against noises by using the objective function based on L-2,L-1 norm. Efficient iterative update rules are derived to resolve the network embedding model using the SSKNMF, and the convergence of these rules are strictly proved from the perspective of mathematics. The results from extensive experiments on several real-world networks show that our proposed algorithm is effective and has better performance than the existing representative methods.
Keywords:
Kernel method
network analysis
network embedding
nonnegative matrix factorization
semi-supervised learning
Journal
IF:
3.6
Papers:
9.8W
Citations:
31.1W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveInsurance activity and economic performance: Fresh evidence from asymmetric panel causality testsOAAI
OAAIShareSavePassive signal propagation and membrane properties in median photoreceptors of the giant barnacleOAAI
OAAIShareSaveShareSave
View more