Unverified50% confidenceFactExact time
The limitations of GNNs are: training depends on high-quality graph-structured data, scaling up the graph easily runs into problems such as neighbor explosion and over-smoothing, and unlike LLMs they cannot directly self-supervised learn from massive amounts of unlabeled text.
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50%
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Long-term
Relevance
10/16/2026
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UnverifiedTraining datasets in the cheminformatics field (such as the publicly available GNPS spectral library) are relatively limited in scale and uneven in spectral quality, and under data-limited conditions, memory- and retrieval-based methods tend to have a natural advantage.74% similarUnverifiedEarly GCNs are essentially transductive—they require seeing the complete graph structure during training and cannot handle new nodes that did not appear in the training set during inference.72% similarUnverifiedThe limitation of formal benchmarks is that their questions are fixed, data may have leaked into the training set, and high scores do not necessarily mean a good user experience.70% similar
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