[HTML][HTML] A perturbation-based framework for link prediction via non-negative matrix factorization
Many link prediction methods have been developed to infer unobserved links or predict
latent links based on the observed network structure. However, due to network noises and …
latent links based on the observed network structure. However, due to network noises and …
[HTML][HTML] A unified framework for link prediction based on non-negative matrix factorization with coupling multivariate information
Many link prediction methods have been developed to infer unobserved links or predict
missing links based on the observed network structure that is always incomplete and subject …
missing links based on the observed network structure that is always incomplete and subject …
Link prediction by deep non-negative matrix factorization
G Chen, H Wang, Y Fang, L Jiang - Expert Systems with Applications, 2022 - Elsevier
Link prediction aims to predict missing links or eliminate spurious links and new links in
future network by known network structure information. Most existing link prediction methods …
future network by known network structure information. Most existing link prediction methods …
Nonnegative matrix factorization for link prediction in directed complex networks using PageRank and asymmetric link clustering information
G Chen, C Xu, J Wang, J Feng, J Feng - Expert Systems with Applications, 2020 - Elsevier
The aim of link prediction is to predict missing links in current networks or new links in future
networks. Almost all the existing directed link prediction algorithms only take into account the …
networks. Almost all the existing directed link prediction algorithms only take into account the …
[HTML][HTML] Similarity-based regularized latent feature model for link prediction in bipartite networks
Link prediction is an attractive research topic in the field of data mining and has significant
applications in improving performance of recommendation system and exploring evolving …
applications in improving performance of recommendation system and exploring evolving …
Link prediction using deep autoencoder-like non-negative matrix factorization with L21-norm
T Li, R Zhang, Y Yao, Y Liu, J Ma - Applied Intelligence, 2024 - Springer
Link prediction aims to predict missing links or eliminate spurious links and anticipate new
links by analyzing observed network topological structure information. Non-negative matrix …
links by analyzing observed network topological structure information. Non-negative matrix …
A novel link prediction algorithm based on inductive matrix completion
Link prediction refers to predicting the connection probability between two nodes in terms of
existing observable network information, such as network structural topology and node …
existing observable network information, such as network structural topology and node …
Link prediction using node information on local paths
Link prediction is one of the most important and challenging tasks in complex network
analysis, which aims to predict missing link based on existing ones in a network. This …
analysis, which aims to predict missing link based on existing ones in a network. This …
Robust non-negative matrix factorization for link prediction in complex networks using manifold regularization and sparse learning
G Chen, C Xu, J Wang, J Feng, J Feng - Physica A: Statistical Mechanics …, 2020 - Elsevier
The aim of link prediction is to disclose the underlying evolution mechanism of networks,
which could be utilized to predict missing links or eliminate spurious links. However, real …
which could be utilized to predict missing links or eliminate spurious links. However, real …
Kernel framework based on non-negative matrix factorization for networks reconstruction and link prediction
Link prediction aims to extract missing informations, identify spurious interactions and
potential informations in complex networks. Similarity-based methods, maximum likelihood …
potential informations in complex networks. Similarity-based methods, maximum likelihood …
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