HERALD framework improves graph condensation for heterophilic data

A new framework called HERALD has been developed for graph condensation, a process that creates a smaller graph to represent a larger one while maintaining performance on downstream tasks. Unlike previous methods that assume homophily (adjacent nodes share labels), HERALD is designed to handle heterophily (adjacent nodes may have different labels). It achieves this by adapting node scoring and feature selection based on the graph's measured heterophily, using criteria like Fisher-discriminability and Local Intrinsic Dimensionality. Experiments show HERALD performs competitively or better than existing methods on heterophilic datasets across various graph neural network architectures. AI

RANK_REASON The cluster contains an academic paper detailing a new method for graph condensation. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Fisher-discriminability
• graph neural networks
• HERALD
• Weisfeiler-Lehman

• paper
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