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ARXIV:2605.10529 · CONTINUAL GRAPH LEARNING · SUBMITTED 12 MAY · 20:15 UTC · FRESHNESS FRESH
ARXIV:2605.10529CONTINUAL GRAPH LEARNINGSUBMITTED 12 MAY · 20:15 UTCFRESHNESS FRESHYousef A. Radwan · Yao Li · Qing Qing · Ziqi Xu · Xingtong Yu · Jiaxing Huang · +2 at arXiv
A benchmark and evaluation framework for continual learning on evolving biomedical knowledge graphs, addressing the limitations of static datasets and synthetic splits.
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Pain A benchmark and evaluation framework for continual learning on evolving biomedical knowledge graphs, addressing the limitations of static datasets and synthetic splits.
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A benchmark and evaluation framework for continual learning on evolving biomedical knowledge graphs, addressing the limitations of static datasets and synthetic splits. Yet existing continual graph learning has been studied almost exclusively on synthetic…
Biomedical knowledge graphs underwrite drug repurposing and clinical decision support, yet the upstream ontologies they depend on update on independent cycles that add millions of edges and deprecate hundreds of thousands more between releases.…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Biomedical knowledge graphs underwrite drug repurposing and clinical decision support, yet the upstream ontologies they depend on update on independent cycles that add millions…
Continual Graph Learning moved forward this cycle; last verified May 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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A benchmark and evaluation framework for continual learning on evolving biomedical knowledge graphs, addressing the limitations of static datasets and synthetic splits.
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10.48550/arXiv.2605.10529A benchmark and evaluation framework for continual learning on evolving biomedical knowledge graphs, addressing the limitations of static datasets and synthetic splits.
Abstract
Biomedical knowledge graphs underwrite drug repurposing and clinical decision support, yet the upstream ontologies they depend on update on independent cycles that add millions of edges and deprecate hundreds of thousands more between releases. Yet existing continual graph learning has been studied almost exclusively on synthetic random splits of static, generic KGs, a regime that cannot reproduce the asynchronous, structured evolution real biomedical KGs undergo. To this end, we introduce PrimeKG-CL, a CGL benchmark built from nine authoritative biomedical databases (129K+ nodes, 8.1M+ edges, 10 node types, 30 relation types) with two genuine temporal snapshots (June 2021, July 2023; 5.83M edges added, 889K removed, 7.21M persistent), 10 entity-type-grouped tasks, multimodal node features, and a per-task persistent/added/removed test stratification. On three tasks (biomedical relationship prediction, entity classification, KGQA), we evaluate six CL strategies across four KGE decoders, plus LKGE, an LLM-RAG agent, and CMKL. We find that decoder choice and continual learning strategy interact strongly: no single strategy performs best across all decoders, and mismatched combinations can significantly degrade performance. Moreover, only DistMult exhibits a clear separation between persistent and deprecated knowledge, indicating that standard metrics conflate retention of still-valid facts with failure to forget outdated ones; this effect is absent under RotatE. In addition, multimodal features improve entity-level tasks by up to 60%, and a recent CKGE framework (IncDE) failed to scale to our 5.67M-triple base task across five attempts up to 350GB RAM. Data, pipeline, baselines, and the stratified split are released openly. Dataset:huggingface.co/datasets/yradwan147/PrimeKGCL|Code:github.com/yradwan147/primekg-cl-neurips2026
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PROBLEM
A benchmark and evaluation framework for continual learning on evolving biomedical knowledge graphs, addressing the limitations of static datasets and synthetic splits. Yet existing continual graph learning has been studied almost exclusively on synthetic random splits of static...
METHOD
Biomedical knowledge graphs underwrite drug repurposing and clinical decision support, yet the upstream ontologies they depend on update on independent cycles that add millions of edges and deprecate hundreds of thousands more between releases. Yet existing continual graph learn...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Biomedical knowledge graphs underwrite drug repurposing and clinical decision support, yet the upstream ontologies they depend on update on independent cycles that add millions of edges and deprecate hund...
WHY NOW
Continual Graph Learning moved forward this cycle; last verified May 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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A benchmark and evaluation framework for continual learning on evolving biomedical knowledge graphs, addressing the limitations of static datasets and synthetic splits. Yet existing continual graph learning has been studied almost exclusively on synthetic random splits of static, generic KGs, a regime that cannot reproduce the asynchronous, structured evolution real biomedical KGs undergo.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Biomedical knowledge graphs underwrite drug repurposing and clinical decision support, yet the upstream ontologies they depend on update on independent cycles that add millions of edges and deprecate hundreds of thousands more between releases. Yet existing continual graph learning has been studied almost exclusively on synthetic random splits of static, generic KGs, a regime that cannot reproduce the asynchronous, structured evolution real biomedical KGs undergo.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Biomedical knowledge graphs underwrite drug repurposing and clinical decision support, yet the upstream ontologies they depend on update on independent cycles that add millions of edges and deprecate hundreds of thousands more between releases. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Continual Graph Learning moved forward this cycle; last verified May 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A benchmark and evaluation framework for continual learning on evolving biomedical knowledge graphs, addressing the limitations of static datasets and synthetic splits.
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