ﻻ يوجد ملخص باللغة العربية
In this note, we study the expander decomposition problem in a more general setting where the input graph has positively weighted edges and nonnegative demands on its vertices. We show how to extend the techniques of Chuzhoy et al. (FOCS 2020) to this wider setting, obtaining a deterministic algorithm for the problem in almost-linear time.
In the decremental single-source shortest paths problem, the goal is to maintain distances from a fixed source $s$ to every vertex $v$ in an $m$-edge graph undergoing edge deletions. In this paper, we conclude a long line of research on this problem
We give almost-linear-time algorithms for constructing sparsifiers with $n poly(log n)$ edges that approximately preserve weighted $(ell^{2}_2 + ell^{p}_p)$ flow or voltage objectives on graphs. For flow objectives, this is the first sparsifier const
We present improved distributed algorithms for triangle detection and its variants in the CONGEST model. We show that Triangle Detection, Counting, and Enumeration can be solved in $tilde{O}(n^{1/2})$ rounds. In contrast, the previous state-of-the-ar
We design an algorithm for computing connectivity in hypergraphs which runs in time $hat O_r(p + min{lambda^{frac{r-3}{r-1}} n^2, n^r/lambda^{r/(r-1)}})$ (the $hat O_r(cdot)$ hides the terms subpolynomial in the main parameter and terms that depend o
An $(epsilon,phi)$-expander decomposition of a graph $G=(V,E)$ is a clustering of the vertices $V=V_{1}cupcdotscup V_{x}$ such that (1) each cluster $V_{i}$ induces subgraph with conductance at least $phi$, and (2) the number of inter-cluster edges i