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 VLDB Endowment , volume =
2 Pith papers cite this work. Polarity classification is still indexing.
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A triangle-message GNN for multicut outperforms heuristics in solution quality on graphs up to 200 nodes and finds optimal solutions faster than exact solvers for some cases.
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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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Graph Neural Networks with Triangle-Based Messages for the Multicut Problem
A triangle-message GNN for multicut outperforms heuristics in solution quality on graphs up to 200 nodes and finds optimal solutions faster than exact solvers for some cases.