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These algorithms provide rigorous theoretical guarantees in probabilistic relative and additive errors of Swing values and can achieve orders of magnitude speed-up over competitors in terms of computational time while offering the same approximate and top-$K query result quality, and in particular, ASC is highly efficient on massive graphs including the billion-edge Yambda and MAG datasets.
LLMs can drastically improve entity resolution accuracy, but only if you intelligently integrate them into a global graph refinement process that minimizes query costs.
Compress recommender system embedding tables by 75% with minimal accuracy loss using a balanced co-clustering approach that's also orders of magnitude faster than alternatives.
