kaal:claim:4855607-010
GNN scalability on large real world graphs is a genuine trade off rather than an engineering gap: sampling methods lose influential neighbors while clustering methods lose structural patterns, so each remedy sacrifices part of the signal the model needs.
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Scalability is a major concern for GNNs on large real-world graphs, as sampling methods may lose influential neighbors while clustering methods may lose structural patterns.
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failuresupport: evidencedfailure: Sampling versus clustering trade offfamily: scalability-and-throughput-limitai-and-agentscitation-and-knowledge
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