Link prediction on complex networks: an experimental survey
Complex networks have been used widely to model a large number of relationships. The
outbreak of COVID-19 has had a huge impact on various complex networks in the real …
outbreak of COVID-19 has had a huge impact on various complex networks in the real …
Dynamic network link prediction with node representation learning from graph convolutional networks
P Mei, YH Zhao - Scientific Reports, 2024 - nature.com
Dynamic network link prediction is extensively applicable in various scenarios, and it has
progressively emerged as a focal point in data mining research. The comprehensive and …
progressively emerged as a focal point in data mining research. The comprehensive and …
gGATLDA: lncRNA-disease association prediction based on graph-level graph attention network
L Wang, C Zhong - BMC bioinformatics, 2022 - Springer
Abstract Background Long non-coding RNAs (lncRNAs) are related to human diseases by
regulating gene expression. Identifying lncRNA-disease associations (LDAs) will contribute …
regulating gene expression. Identifying lncRNA-disease associations (LDAs) will contribute …
Elementary subgraph features for link prediction with neural networks
Z Fang, S Tan, Y Wang, J Lü - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
The enclosing subgraph of a target link has been proved to be effective for prediction of
potential links. However, it is still unclear what topological features of the subgraph play the …
potential links. However, it is still unclear what topological features of the subgraph play the …
Weak link prediction based on hyper latent distance in complex network
MY Zhou, F Wang, Z Chen, J Wu, G Liu… - Expert Systems with …, 2024 - Elsevier
Weak links play a crucial role in the functionality and dynamics of networks. Nevertheless,
the ability to forecast weak links accurately remains elusive. This article introduces a neural …
the ability to forecast weak links accurately remains elusive. This article introduces a neural …
Dynamic network link prediction by learning effective subgraphs using CNN-LSTM
K Selvarajah, K Ragunathan, Z Kobti… - … Joint Conference on …, 2020 - ieeexplore.ieee.org
Predicting the future link between nodes is a significant problem in social network analysis,
known as Link Prediction (LP). Recently, dynamic network link prediction has attracted many …
known as Link Prediction (LP). Recently, dynamic network link prediction has attracted many …
[PDF][PDF] Distance-enhanced graph neural network for link prediction
Link prediction, which is to predict the existence of a link/edge between two vertices in a
graph, is a classical problem in machine learning. Intuitively, if it takes a long distance to …
graph, is a classical problem in machine learning. Intuitively, if it takes a long distance to …
Link prediction in heterogeneous networks based on metapath projection and aggregation
Y Zhao, Y Sun, Y Huang, L Li, H Dong - Expert Systems with Applications, 2023 - Elsevier
A heterogeneous network, which contains multiple types of nodes and edges, is a special
kind of network. Link prediction in heterogeneous networks is a consistently interesting …
kind of network. Link prediction in heterogeneous networks is a consistently interesting …
Link prediction in multiplex networks: An evidence theory method
H Luo, L Li, H Dong, X Chen - Knowledge-Based Systems, 2022 - Elsevier
Due to its broad range of applications, link prediction has captured considerable attention
from various disciplines. In this paper, we focus on the problem of link prediction in multiplex …
from various disciplines. In this paper, we focus on the problem of link prediction in multiplex …
Hrotate: Hybrid relational rotation embedding for knowledge graph
Knowledge Graph represents the real world's information in the form of triplets (head,
relation, and tail). However, most Knowledge Graphs are highly incomplete. The goal of a …
relation, and tail). However, most Knowledge Graphs are highly incomplete. The goal of a …