Saddle-point asymptotics for chromatic and Tutte polynomials of complete multipartite graphs prove Kotesovec's fixed-column conjecture and yield fixed-part and all-order expansions for OEIS sequences.
hub
Modern Graph Theory
4 Pith papers cite this work, alongside 1,877 external citations. Polarity classification is still indexing.
4
Pith papers citing it
1,877
external citations · Crossref
hub tools
citation-role summary
background 1
citation-polarity summary
years
2026 4verdicts
UNVERDICTED 4roles
background 1polarities
background 1representative citing papers
Polynomial kernels exist for Leaf & Internal-Constrained Diverse Spanning Trees (parameter p+q+k+ℓ) and Leaf & Non-terminal-Constrained Diverse Spanning Trees (parameter p+|V_NT|+k+ℓ).
RLGT is a modular reinforcement learning framework for extremal graph theory that handles undirected, directed, looped, and multi-colored graphs to facilitate future research.
Segmentedness is defined as the complement of edge density in the policy graph, with a sampling-based estimator requiring only 97 random node pairs for a 95% confidence interval of width ±0.2 independent of network size.
citing papers explorer
-
Saddle-Point Asymptotics for Chromatic and Tutte Polynomial Evaluations of Complete Multipartite Graphs
Saddle-point asymptotics for chromatic and Tutte polynomials of complete multipartite graphs prove Kotesovec's fixed-column conjecture and yield fixed-part and all-order expansions for OEIS sequences.
-
Polynomial Kernels for Spanning Tree with Diversity Requirements
Polynomial kernels exist for Leaf & Internal-Constrained Diverse Spanning Trees (parameter p+q+k+ℓ) and Leaf & Non-terminal-Constrained Diverse Spanning Trees (parameter p+|V_NT|+k+ℓ).
-
RLGT: A reinforcement learning framework for extremal graph theory
RLGT is a modular reinforcement learning framework for extremal graph theory that handles undirected, directed, looped, and multi-colored graphs to facilitate future research.
-
How segmented is my network?
Segmentedness is defined as the complement of edge density in the policy graph, with a sampling-based estimator requiring only 97 random node pairs for a 95% confidence interval of width ±0.2 independent of network size.