PrimeKG-CL supplies the first continual graph learning benchmark using authentic temporal snapshots from nine biomedical databases, showing strong interactions between embedding decoders and learning strategies plus limits of standard metrics on retention versus forgetting.
Extended-connectivity fingerprints.Journal of Chemical Information and Modeling, 50(5):742–754
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CMKL delivers a 60% gain in average precision on continual entity classification in a 129K-entity biomedical KG benchmark by fusing multimodal features and protecting against modality-specific forgetting, while relationship prediction stays comparable to baselines.
Enhanced graph-theoretic models with added features and ensembles achieve average R² of 0.79 on five molecular property datasets, matching or outperforming graph convolutional networks.
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PrimeKG-CL: A Continual Graph Learning Benchmark on Evolving Biomedical Knowledge Graphs
PrimeKG-CL supplies the first continual graph learning benchmark using authentic temporal snapshots from nine biomedical databases, showing strong interactions between embedding decoders and learning strategies plus limits of standard metrics on retention versus forgetting.
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CMKL: Modality-Aware Continual Learning for Evolving Biomedical Knowledge Graphs
CMKL delivers a 60% gain in average precision on continual entity classification in a 129K-entity biomedical KG benchmark by fusing multimodal features and protecting against modality-specific forgetting, while relationship prediction stays comparable to baselines.
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Graph-Theoretic Models for the Prediction of Molecular Measurements
Enhanced graph-theoretic models with added features and ensembles achieve average R² of 0.79 on five molecular property datasets, matching or outperforming graph convolutional networks.
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