Abstract:In recent years, semi-supervised graph learning with data augmentation (DA) has been the most commonly used and best-performing method to improve model robustness in the sparse scenarios with few labeled samples. However, most of existing DA methods are based on the homogeneous graph while none are specific for the heterogeneous graph. Differing from the homogeneous graph, DA in heterogeneous graph faces greater challenges: heterogeneity of information requires DA strategies to effectively handle heterogeneous… Show more
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