0000000000060055
AUTHOR
Markus Blumenstock
showing 4 related works from this author
Fast Algorithms for Pseudoarboricity
2015
The densest subgraph problem, which asks for a subgraph with the maximum edges-to-vertices ratio d∗, is solvable in polynomial time. We discuss algorithms for this problem and the computation of a graph orientation with the lowest maximum indegree, which is equal to ⌈d∗⌉. This value also equals the pseudoarboricity of the graph. We show that it can be computed in O(|E| √ log log d∗) time, and that better estimates can be given for graph classes where d∗ satisfies certain asymptotic bounds. These runtimes are achieved by accelerating a binary search with an approximation scheme, and a runtime analysis of Dinitz’s algorithm on flow networks where all arcs, except the source and sink arcs, hav…
A Constructive Arboricity Approximation Scheme
2018
The arboricity $\Gamma$ of a graph is the minimum number of forests its edge set can be partitioned into. Previous approximation schemes were nonconstructive, i.e., they only approximated the arboricity as a value without computing a corresponding forest partition. This is because they operate on the related pseudoforest partitions or the dual problem of finding dense subgraphs. We propose an algorithm for converting a partition of $k$ pseudoforests into a partition of $k+1$ forests in $O(mk\log k + m \log n)$ time with a data structure by Brodal and Fagerberg that stores graphs of arboricity $k$. A slightly better bound can be given when perfect hashing is used. When applied to a pseudofor…
Algorithms for the Maximum Weight Connected $$k$$-Induced Subgraph Problem
2014
Finding differentially regulated subgraphs in a biochemical network is an important problem in bioinformatics. We present a new model for finding such subgraphs which takes the polarity of the edges (activating or inhibiting) into account, leading to the problem of finding a connected subgraph induced by \(k\) vertices with maximum weight. We present several algorithms for this problem, including dynamic programming on tree decompositions and integer linear programming. We compare the strength of our integer linear program to previous formulations of the \(k\)-cardinality tree problem. Finally, we compare the performance of the algorithms and the quality of the results to a previous approac…
A Constructive Arboricity Approximation Scheme
2020
The arboricity \(\varGamma \) of a graph is the minimum number of forests its edge set can be partitioned into. Previous approximation schemes were nonconstructive, i.e., they approximate the arboricity as a value without computing a corresponding forest partition. This is because they operate on pseudoforest partitions or the dual problem of finding dense subgraphs.