New algorithms tackle graph partitioning with demand functions

Researchers have introduced new algorithms for graph partitioning problems that incorporate demand functions. The study focuses on minimizing a metric called generalized conductance, which considers both edge costs and demand between vertices. The proposed algorithms achieve an approximation guarantee of O(log n) for this objective, with improvements to O(sqrt(log n)) for multiplicative demand functions and O(1) for tree structures. AI

IMPACT Introduces new algorithmic approaches for graph partitioning problems that could have applications in machine learning and data analysis.

RANK_REASON The cluster contains a single academic paper detailing new algorithms and theoretical results in graph partitioning. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

• Generalized Conductance
• Generalized Conductance Problem
• Generalized k-Multicut Problem
• Graph Partitioning with Demands
• Hierarchical Clustering with Demands
• Sparsest-Cut Problem

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