
FLHG addresses label sparsity in heterogeneous graphs through few-shot learning methods.


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few-shot-learning-on-heterogeneous-graphs-challenges-progress-and-prospects


Few-shot Learning on Heterogeneous Graphs: Challenges, Progress, and Prospects


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FLHG aims to address label sparsity in heterogeneous graphs by developing methods for effective few-shot learning, categorizing into single-heterogeneity, dual-heterogeneity, and multi-heterogeneity scenarios.


heterogeneous-graph-few-shot-learning-challenges-progress-and-prospects


Heterogeneous Graph Few-shot Learning: Challenges, Progress, and Prospects
