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Physics-Guided Hypergraph Contrastive Learning for Dynamic Hyperedge Prediction
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DOI：10.1109/TNSE.2024.3501378.png)
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摘要
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With the increasing magnitude and complexity of data, the importance of higher-order networks is increasingly prominent. Dynamic hyperedge prediction reveals potential higher-order patterns with time evolution in networks, thus providing beneficial insights for decision making. Nevertheless, most existing neural network-based hyperedge prediction models are limited to static hypergraphs. Furthermore, previous efforts on hypergraph contrastive learning involve augmentation strategies, with insufficient consideration of the higher-order and lower-order views carried by the hypergraph itself. To address the above issues, we propose PCL-HP, a physics-guided hypergraph contrastive learning framework for dynamic hyperedge prediction. Specifically, we simply distinguish higher-order and lower-order views of the hypergraph to perform dynamic hypergraph contrastive learning and obtain abstract and concrete feature information, respectively. For lower-order views, we propose a physics-guided desynchronization mechanism to effectively guide the encoder to fuse the physical information during feature propagation, thus alleviating the problem of feature over-smoothing. Additionally, residual loss is introduced into the optimization process to incrementally quantify the loss at different stages to enhance the learning capability of the model. Extensive experiments on 10 dynamic higher-order datasets indicate that PCL-HP outperforms state-of-the-art baselines.
Keyword:
Contrastive learning
Predictive models
Optimization
Computational modeling
Training
Representation learning
Feature extraction
Mathematical models
Adaptation models
Neurons
Higher-order relation
hyperedge prediction
contrastive learning
desynchronization mechanism
residual loss
期刊
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IF:
7.9
论文数:
2.6K
被引数:
10.0K
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