Deng, X.; Sun, J.; Lu, J. Graph Neural Network-Based Efficient Subgraph Embedding Method for Link Prediction in Mobile Edge Computing. Sensors2023, 23, 4936.
Deng, X.; Sun, J.; Lu, J. Graph Neural Network-Based Efficient Subgraph Embedding Method for Link Prediction in Mobile Edge Computing. Sensors 2023, 23, 4936.
Deng, X.; Sun, J.; Lu, J. Graph Neural Network-Based Efficient Subgraph Embedding Method for Link Prediction in Mobile Edge Computing. Sensors2023, 23, 4936.
Deng, X.; Sun, J.; Lu, J. Graph Neural Network-Based Efficient Subgraph Embedding Method for Link Prediction in Mobile Edge Computing. Sensors 2023, 23, 4936.
Abstract
Link prediction is to complete the missing links in the network or to predict the generation of new links according to the current network structure information, which is very important for mining and analyzing the evolution of the network such for construction and analysis of logical architecture in 6G network. Link prediction algorithms based on node similarity need predefined similarity functions, which is highly hypothetical and only applies to specific network structures without generality. To solve this problem, this paper proposes a link prediction algorithm based on the subgraph of the target node pair. In order to automatically learn the graph structure characteristics, the algorithm firstly extracts the h-hop subgraph of the target node pair, and then predicts whether the target node pair will be linked according to the subgraph. Experiments on seven real data sets show that the link prediction algorithm based on target node pair subgraph is suitable for various network structures and superior to other link prediction algorithms.
Keywords
link prediction; graph neural network; graph embedding
Subject
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
