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In the laminar-constrained spanning tree problem, the goal is to find a minimum-cost spanning tree which respects upper bounds on the number of times each cut in a given laminar family is crossed. This generalizes the well-studied…

Data Structures and Algorithms · Computer Science 2023-04-18 Nathan Klein , Neil Olver

The trace $\tr(q(\ma{L} + q\ma{I})^{-1})$, where $\ma{L}$ is a symmetric diagonally dominant matrix, is the quantity of interest in some machine learning problems. However, its direct computation is impractical if the matrix size is large.…

Signal Processing · Electrical Eng. & Systems 2022-09-14 Yusuf Yigit Pilavci , Pierre-Olivier Amblard , Simon Barthelme , Nicolas Tremblay

The tree-cut width of a graph is a graph parameter defined by Wollan [J. Comb. Theory, Ser. B, 110:47-66, 2015] with the help of tree-cut decompositions. In certain cases, tree-cut width appears to be more adequate than treewidth as an…

Data Structures and Algorithms · Computer Science 2018-05-16 Eunjung Kim , Sang-il Oum , Christophe Paul , Ignasi Sau , Dimitrios M. Thilikos

In this paper we describe a randomized algorithm which returns a maximal spanning forest of an unknown {\em weighted} undirected graph making $O(n)$ $\mathsf{CUT}$ queries in expectation. For weighted graphs, this is optimal due to a result…

Data Structures and Algorithms · Computer Science 2023-06-21 Hang Liao , Deeparnab Chakrabarty

We consider all spanning trees of a complete simple graph $\Gamma$ on $n$ vertices that contain a given $m-$forest $F$. We show that the number of such spanning trees, $\tau(F)$, doesn't depend on the structure of $F$ and is completely…

Combinatorics · Mathematics 2022-10-18 Peter J. Cameron , Michael Kagan

This work describes probabilistic methods for utilizing random spanning trees generated via a random walk process. Goyal et al. showed that the union of random spanning trees approximates the expansion of every cut of a graph. First, we…

Networking and Internet Architecture · Computer Science 2019-10-16 Shlomi Dolev , Daniel Khankin

Random Forests are one of the most popular classifiers in machine learning. The larger they are, the more precise is the outcome of their predictions. However, this comes at a cost: their running time for classification grows linearly with…

Machine Learning · Computer Science 2019-12-24 Frederik Gossen , Bernhard Steffen

In spatially embedded networks such as transportation and power grids, understanding how edge removals affect connectivity is crucial for robustness analysis. This paper studies a planar graph dismantling problem under an edge-budget…

Social and Information Networks · Computer Science 2025-11-13 Fangchen You

It is well-known that the number of spanning trees, denoted by $\tau(G)$, in a connected multi-graph $G$ can be calculated by the Matrix-Tree theorem and Tutte's deletion-contraction theorem. In this short note, we find an alternate method…

Combinatorics · Mathematics 2021-10-13 Fengming Dong , Jun Ge , Zhangdong Ouyang

A vertex whose removal in a graph $G$ increases the number of components of $G$ is called a cut vertex. For all $n,c$, we determine the maximum number of connected induced subgraphs in a connected graph with order $n$ and $c$ cut vertices,…

Combinatorics · Mathematics 2019-10-11 Audace A. V. Dossou-Olory

We compute the magnitude (an isometric invariant of metric spaces) of compact $\mathbb{R}$-trees and show that it equals $1 + L/2$, where $L \in [0, \infty]$ denotes the total length. Although length is the only geometric invariant captured…

Metric Geometry · Mathematics 2026-05-06 Philippe Bouafia

In this paper algebraic and combinatorial properties and a computation of the number of the spanning trees are developed for certain graphs. To this purpose, an original method, independent of the spectrum of the Laplacian matrix associated…

Combinatorics · Mathematics 2024-04-01 Maurizio Imbesi , Monica La Barbiera , Santo Saraceno

We introduce the graph theoretical parameter of edge treewidth. This parameter occurs in a natural way as the tree-like analogue of cutwidth or, alternatively, as an edge-analogue of treewidth. We study the combinatorial properties of…

Discrete Mathematics · Computer Science 2021-12-15 Loïc Magne , Christophe Paul , Abhijat Sharma , Dimitrios M. Thilikos

Let $T$ be a weighted tree. The weight of a subtree $T_1$ of $T$ is defined as the product of weights of vertices and edges of $T_1$. We obtain a linear-time algorithm to count the sum of weights of subtrees of $T$. As applications, we…

Combinatorics · Mathematics 2007-05-23 Weigen Yan , Yeong-Nan Yeh

With applications in distribution systems and communication networks, the minimum stretch spanning tree problem is to find a spanning tree T of a graph G such that the maximum distance in T between two adjacent vertices is minimized. The…

Combinatorics · Mathematics 2017-12-12 Lan Lin , Yixun Lin

Given a tree of weighted vertices, it is sometimes possible to break the tree into two equally-weighted subtrees within an allowable error. We give a fast algorithm that finds an edge which breaks the tree into equal-weight components or…

Combinatorics · Mathematics 2020-11-13 Corinne Mulvey

We present a simple yet rigorous approach to the determination of the spectral dimension of random trees, based on the study of the massless limit of the Gaussian model on such trees. As a byproduct, we obtain evidence in favor of a new…

Condensed Matter · Physics 2008-11-26 C. Destri , L. Donetti

We give combinatorial criteria for predicting the transcendental weight of Feynman integrals of certain graphs in $\phi^4$ theory. By studying spanning forest polynomials, we obtain operations on graphs which are weight-preserving, and a…

Mathematical Physics · Physics 2011-01-17 Francis Brown , Karen Yeats

When considering the number of subtrees of trees, the extremal structures which maximize this number among binary trees and trees with a given maximum degree lead to some interesting facts that correlate to other graphical indices in…

Combinatorics · Mathematics 2012-10-11 Xiu-Mei Zhang , Xiao-Dong Zhang , Daniel Gray , Hua Wang

A random forest prediction can be computed by the scalar product of the labels of the training examples and a set of weights that are determined by the leafs of the forest into which the test object falls; each prediction can hence be…

Machine Learning · Computer Science 2023-11-27 Henrik Boström
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