Establishes Ω(n/ε²) query lower bounds for approximating correlation clustering cost and partitions under memory constraints in adjacency-matrix and general graph models.
Proceedings of the 57th Annual ACM Symposium on Theory of Computing (STOC) , pages =
2 Pith papers cite this work. Polarity classification is still indexing.
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Node-weighted triangle detection can be solved in optimal O(MM(n)) time with a simpler algorithm than previous work.
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Query Lower Bounds for Correlation Clustering under Memory Constraints
Establishes Ω(n/ε²) query lower bounds for approximating correlation clustering cost and partitions under memory constraints in adjacency-matrix and general graph models.
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Node-Weighted Triangles: Faster and Simpler
Node-weighted triangle detection can be solved in optimal O(MM(n)) time with a simpler algorithm than previous work.