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Found 5 papers, 2 papers with code
Exploiting Latent Attribute Interaction with Transformer on Heterogeneous Information Networks
In addition, most of them only consider the interactions between nodes while neglecting the high-order information behind the latent interactions among different node features.
Enhancing Graph Neural Networks with Structure-Based Prompt
Graph Neural Networks (GNNs) are powerful in learning semantics of graph data.
Layout-aware Webpage Quality Assessment
To assess webpage quality from complex DOM tree data, we propose a graph neural network (GNN) based method that extracts rich layout-aware information that implies webpage quality in an end-to-end manner.
Geometry Contrastive Learning on Heterogeneous Graphs
Self-supervised learning (especially contrastive learning) methods on heterogeneous graphs can effectively get rid of the dependence on supervisory data.
Deep Active Learning for Anchor User Prediction
Due to the expensive costs of labeling anchor users for training prediction models, we consider in this paper the problem of minimizing the number of user pairs across multiple networks for labeling as to improve the accuracy of the prediction.
