LogosKG delivers a novel hardware-aligned system for efficient multi-hop retrieval on billion-edge knowledge graphs without sacrificing fidelity, demonstrated via biomedical KG-LLM applications.
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Simulations show local evolution rules generate higher shortest path multiplicity than random graphs with the same degrees, increasing with size for power-law distributions and correlating with community count.
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LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval
LogosKG delivers a novel hardware-aligned system for efficient multi-hop retrieval on billion-edge knowledge graphs without sacrificing fidelity, demonstrated via biomedical KG-LLM applications.
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Local network evolution rules drive shortest path multiplicity
Simulations show local evolution rules generate higher shortest path multiplicity than random graphs with the same degrees, increasing with size for power-law distributions and correlating with community count.