Position: Graph Condensation Needs a Reset -- Move Beyond Full-dataset Training and Model-Dependence
A new position paper argues that the current methods for graph condensation, a technique aimed at making Graph Neural Networks (GNNs) more scalable, are fundamentally flawed. The paper highlights that existing approaches require training on the full dataset, negating efficiency gains, and suffer from high computational costs and poor generalization across different GNN architectures. The authors call for a reset in the field, advocating for lightweight, architecture-agnostic methods that can be practically deployed to achieve true efficiency in GNN training. AI
IMPACT Critiques current graph condensation methods, potentially redirecting research towards more efficient and practical GNN scalability solutions.