Hypergraph Neural Networks Based on Enclosing Subgraph Extraction for Link Prediction

• Published in: Proceedings - International Conference on Parallel and Distributed Systems pp. 455 - 461
• Main Authors: Chen, Liang, Zhao, Ying, Sajjanhar, Atul
• Format: Conference Proceeding
• Language: English
• Published: IEEE 10.10.2024
• Subjects: Accuracy; Complexity theory; Convolutional neural networks; dual hypergraph transformation; Feature extraction; Graph neural networks; hypergraph; hypergraph neural networks; link prediction; Memory management; Predictive models; Runtime
• ISSN: 2690-5965
• DOI: 10.1109/ICPADS63350.2024.00066

Recently, the link prediction methods based on enclosing subgraph extraction and line graph transformation have been proven to achieve excellent prediction accuracy, but there are still some shortcomings, for examples, the time and space complexity of line graph transformation is too high and the graph neural network it used ignores the high-order relationship and local clustering structure between nodes, which makes it difficult to be widely used in real life and may affect the prediction accuracy. To solve the above problems, a hypergraph neural network model based on enclosing subgraph extraction is proposed, which converts subgraph into hypergraph by dual hypergraph transformation, and uses the hypergraph convolutional neural network to learn the higher-order features of nodes and edges respectively. After three experiments, the results show that the proposed model not only has higher prediction accuracy, but also has shorter runtime and less memory usage.