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1. Better Hardness Results for the Minimum Spanning Tree Congestion Problem

   https://doi.org/10.1007/s00453-024-01278-5  

   Luu, Huong; Chrobak, Marek (January 2025, Algorithmica)

   Abstract In the spanning tree congestion problem, given a connected graphG, the objective is to compute a spanning treeTinGthat minimizes its maximum edge congestion, where the congestion of an edgeeofTis the number of edges inGfor which the unique path inTbetween their endpoints traversese. The problem is known to be$$\mathbb{N}\mathbb{P}$$ $NP$-hard, but its approximability is still poorly understood, and it is not even known whether the optimum solution can be efficiently approximated with ratioo(n). In the decision version of this problem, denoted$${\varvec{K}-\textsf {STC}}$$ $K-\mathrm{STC}$, we need to determine ifGhas a spanning tree with congestion at mostK. It is known that$${\varvec{K}-\textsf {STC}}$$ $K-\mathrm{STC}$is$$\mathbb{N}\mathbb{P}$$ $NP$-complete for$$K\ge 8$$ $K\ge 8$, and this implies a lower bound of 1.125 on the approximation ratio of minimizing congestion. On the other hand,$${\varvec{3}-\textsf {STC}}$$ $3-\mathrm{STC}$can be solved in polynomial time, with the complexity status of this problem for$$K\in { \left\{ 4,5,6,7 \right\} }$$ $K\in \left(4,,,5,,,6,,,7\right)$remaining an open problem. We substantially improve the earlier hardness results by proving that$${\varvec{K}-\textsf {STC}}$$ $K-\mathrm{STC}$is$$\mathbb{N}\mathbb{P}$$ $NP$-complete for$$K\ge 5$$ $K\ge 5$. This leaves only the case$$K=4$$ $K=4$open, and improves the lower bound on the approximation ratio to 1.2. Motivated by evidence that minimizing congestion is hard even for graphs of small constant radius, we also consider$${\varvec{K}-\textsf {STC}}$$ $K-\mathrm{STC}$restricted to graphs of radius 2, and we prove that this variant is$$\mathbb{N}\mathbb{P}$$ $NP$-complete for all$$K\ge 6$$ $K\ge 6$. 

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2. Polynomial Bounds for Chromatic Number. IV: A Near-polynomial Bound for Excluding the Five-vertex Path

   https://doi.org/10.1007/s00493-023-00015-w  

   Scott, Alex; Seymour, Paul; Spirkl, Sophie (October 2023, Combinatorica)

   Abstract A graphGisH-freeif it has no induced subgraph isomorphic toH. We prove that a$$P_5$$ $P_5$-free graph with clique number$$\omega \ge 3$$ $\omega \ge 3$has chromatic number at most$$\omega ^{\log _2(\omega )}$$ ${\omega }^{{log}_2\left(\omega \right)}$. The best previous result was an exponential upper bound$$(5/27)3^{\omega }$$ $(5/27)3^{\omega }$, due to Esperet, Lemoine, Maffray, and Morel. A polynomial bound would imply that the celebrated Erdős-Hajnal conjecture holds for$$P_5$$ $P_5$, which is the smallest open case. Thus, there is great interest in whether there is a polynomial bound for$$P_5$$ $P_5$-free graphs, and our result is an attempt to approach that. 

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3. eGHWT: The Extended Generalized Haar–Walsh Transform

   https://doi.org/10.1007/s10851-021-01064-w  

   Saito, Naoki; Shao, Yiqun (January 2022, Journal of Mathematical Imaging and Vision)

   Abstract Extending computational harmonic analysis tools from the classical setting of regular lattices to the more general setting of graphs and networks is very important, and much research has been done recently. The generalized Haar–Walsh transform (GHWT) developed by Irion and Saito (2014) is a multiscale transform for signals on graphs, which is a generalization of the classical Haar and Walsh–Hadamard transforms. We propose theextendedgeneralized Haar–Walsh transform (eGHWT), which is a generalization of the adapted time–frequency tilings of Thiele and Villemoes (1996). The eGHWT examines not only the efficiency of graph-domain partitions but also that of “sequency-domain” partitionssimultaneously. Consequently, the eGHWT and its associated best-basis selection algorithm for graph signals significantly improve the performance of the previous GHWT with the similar computational cost,$$O(N \log N)$$ $O(NlogN)$, whereNis the number of nodes of an input graph. While the GHWT best-basis algorithm seeks the most suitable orthonormal basis for a given task among more than$$(1.5)^N$$ ${\left(1.5\right)}^N$possible orthonormal bases in$$\mathbb {R}^N$$ $R^N$, the eGHWT best-basis algorithm can find a better one by searching through more than$$0.618\cdot (1.84)^N$$ $0.618·{\left(1.84\right)}^N$possible orthonormal bases in$$\mathbb {R}^N$$ $R^N$. This article describes the details of the eGHWT best-basis algorithm and demonstrates its superiority using several examples including genuine graph signals as well as conventional digital images viewed as graph signals. Furthermore, we also show how the eGHWT can be extended to 2D signals and matrix-form data by viewing them as a tensor product of graphs generated from their columns and rows and demonstrate its effectiveness on applications such as image approximation. 

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4. A folding preprocess for the max k-cut problem

   https://doi.org/10.1007/s11590-025-02199-0  

   Fakhimi, Ramin; Validi, Hamidreza; Hicks, Illya_V; Terlaky, Tamás; Zuluaga, Luis_F (April 2025, Optimization Letters)

   Abstract Given graph$$G=(V,E)$$ $G=(V,E)$with vertex setVand edge setE, the maxk-cut problem seeks to partition the vertex setVinto at mostksubsets that maximize the weight (number) of edges with endpoints in different parts. This paper proposes a graph folding procedure (i.e., a procedure that reduces the number of the vertices and edges of graphG) for the weighted maxk-cut problem that may help reduce the problem’s dimensionality. While our theoretical results hold for any$$k \ge 2$$ $k\ge 2$, our computational results show the effectiveness of the proposed preprocessonlyfor$$k=2$$ $k=2$and on two sets of instances. Furthermore, we observe that the preprocess improves the performance of a MIP solver on a set of large-scale instances of the max cut problem. 

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5. Rates of convergence for regression with the graph poly-Laplacian

   https://doi.org/10.1007/s43670-023-00075-5  

   Trillos, Nicolás García; Murray, Ryan; Thorpe, Matthew (November 2023, Sampling Theory, Signal Processing, and Data Analysis)

   Abstract In the (special) smoothing spline problem one considers a variational problem with a quadratic data fidelity penalty and Laplacian regularization. Higher order regularity can be obtained via replacing the Laplacian regulariser with a poly-Laplacian regulariser. The methodology is readily adapted to graphs and here we consider graph poly-Laplacian regularization in a fully supervised, non-parametric, noise corrupted, regression problem. In particular, given a dataset$$\{x_i\}_{i=1}^n$$ ${\left\{x_i\right\}}_{i=1}^n$and a set of noisy labels$$\{y_i\}_{i=1}^n\subset \mathbb {R}$$ ${\left\{y_i\right\}}_{i=1}^n\subset R$we let$$u_n{:}\{x_i\}_{i=1}^n\rightarrow \mathbb {R}$$ $u_n:{\left\{x_i\right\}}_{i=1}^n\to R$be the minimizer of an energy which consists of a data fidelity term and an appropriately scaled graph poly-Laplacian term. When$$y_i = g(x_i)+\xi _i$$ $y_i=g\left(x_i\right)+{\xi }_i$, for iid noise$$\xi _i$$ ${\xi }_i$, and using the geometric random graph, we identify (with high probability) the rate of convergence of$$u_n$$ $u_n$togin the large data limit$$n\rightarrow \infty $$ $n\to \infty$. Furthermore, our rate is close to the known rate of convergence in the usual smoothing spline model. 

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