Graph Normalization is a convergent dynamical system that approximates MWIS by always reaching a binary maximum independent set via majorization-minimization and evolutionary game equivalence.
Reducibility among combinatorial problems
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
UN-CCDs extend Cluster Catch Digraphs by using nearest-neighbor-distance Monte Carlo tests instead of Ripley's K to determine covering radii, yielding competitive performance on moderate-dimensional data with complex clusters and uniform noise.
citing papers explorer
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Graph Normalization: Fast Binarizing Dynamics for Differentiable MWIS
Graph Normalization is a convergent dynamical system that approximates MWIS by always reaching a binary maximum independent set via majorization-minimization and evolutionary game equivalence.
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Clustering with Uniformity- and Neighbor-Based Random Geometric Graphs
UN-CCDs extend Cluster Catch Digraphs by using nearest-neighbor-distance Monte Carlo tests instead of Ripley's K to determine covering radii, yielding competitive performance on moderate-dimensional data with complex clusters and uniform noise.