New HGC-RC Framework Simplifies Training of Heterogeneous Graph Neural Networks

Researchers have introduced HGC-RC, a novel framework designed to make training Heterogeneous Graph Neural Networks (HGNNs) more efficient on large datasets. Existing graph condensation methods are often unsuitable for heterogeneous graphs, relying on computationally intensive techniques. HGC-RC addresses this by first generating enhanced node embeddings through lightweight propagation. It then employs a hybrid clustering strategy that partitions labeled nodes by class and clusters unlabeled nodes by type to maintain crucial connections. This approach allows for the reconstruction of a compact heterogeneous graph, significantly accelerating HGNN training without compromising performance. AI

IMPACT This framework offers a practical solution for accelerating the training of HGNNs on large-scale heterogeneous graphs, potentially enabling broader application of these models.

RANK_REASON The cluster contains a research paper detailing a new method for improving the efficiency of Heterogeneous Graph Neural Networks. [lever_c_demoted from research: ic=1 ai=1.0]

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• cs.LG
• Heterogeneous Graph Neural Networks
• HGC-RC

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