kaal:claim:4855607-012
Most GNN architectures assume homogeneous graph structures, so adapting them to heterogeneous graphs with diverse node and edge types remains an unsolved research challenge, and full batch training on large graphs suffers memory overflow.
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Training GNNs on large graphs can be resource-intensive, and full-batch training methods suffer from memory overflow issues. Many GNNs assume homogeneous graph structures, and adapting these models to heterogeneous graphs with diverse node and edge types remains a significant research challenge.
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failuresupport: evidencedfailure: Homogeneous graph assumptionfamily: ai-model-and-training-failureai-and-agentscitation-and-knowledgeeducation-and-practice
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