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 …
Inductive link prediction with interactive structure learning on attributed graph
Link prediction is one of the most important tasks in graph machine learning, which aims at
predicting whether two nodes in a network have an edge. Real-world graphs typically …
predicting whether two nodes in a network have an edge. Real-world graphs typically …
Generative graph neural networks for link prediction
Inferring missing links or detecting spurious ones based on observed graphs, known as link
prediction, is a long-standing challenge in graph data analysis. With the recent advances in …
prediction, is a long-standing challenge in graph data analysis. With the recent advances in …
Relpnet: Relation-based link prediction neural network
E Wu, H Cui, Z Chen - Proceedings of the 31st ACM International …, 2022 - dl.acm.org
Node-based link prediction methods have occupied a dominant position in the graph link
prediction task. These methods commonly aggregate node features from the subgraph to …
prediction task. These methods commonly aggregate node features from the subgraph to …
Hyper-substructure enhanced link predictor
Link prediction has long been the focus in the analysis of network-structured data. Though
straightforward and efficient, heuristic approaches like Common Neighbors perform link …
straightforward and efficient, heuristic approaches like Common Neighbors perform link …
Link Prediction on Multilayer Networks through Learning of Within-Layer and Across-Layer Node-Pair Structural Features and Node Embedding Similarity
L Zangari, D Mandaglio, A Tagarelli - Proceedings of the ACM on Web …, 2024 - dl.acm.org
Link prediction has traditionally been studied in the context of simple graphs, although real-
world networks are inherently complex as they are often comprised of multiple …
world networks are inherently complex as they are often comprised of multiple …
Link prediction based on graph neural networks
Link prediction is a key problem for network-structured data. Link prediction heuristics use
some score functions, such as common neighbors and Katz index, to measure the likelihood …
some score functions, such as common neighbors and Katz index, to measure the likelihood …
A Graph Attention Network Based Link Prediction Method Using Link Value Estimation
Z Zhang, X Wu, G Zhu, W Qin, N Liang - IEEE Access, 2023 - ieeexplore.ieee.org
Link prediction in complex networks is a critical process aimed at uncovering hidden or
potential connections among nodes. This technique is widely utilized in areas such as …
potential connections among nodes. This technique is widely utilized in areas such as …
Link prediction based on deep convolutional neural network
In recent years, endless link prediction algorithms based on network representation learning
have emerged. Network representation learning mainly constructs feature vectors by …
have emerged. Network representation learning mainly constructs feature vectors by …
Neighborhood interaction attention network for link prediction
Interactions between neighborhoods of two target nodes are often regarded as important
clues for link prediction. In this paper, we propose a novel link prediction neural model …
clues for link prediction. In this paper, we propose a novel link prediction neural model …