Linear-Time Algorithms for Computing Twinless Strong Articulation Points and Related Problems

TL;DR

This paper introduces the first linear-time algorithms for identifying twinless strong articulation points in directed graphs, with applications in network design and structural analysis.

Contribution

It presents a novel linear-time algorithm for finding twinless strong articulation points and reduces the problem to a vertex-edge cut problem in undirected graphs.

Findings

Linear-time algorithm for twinless strong articulation points.

Reduction of the problem to undirected graph vertex-edge cut-pairs.

Efficient computation of TSCCs after vertex removal.

Abstract

A directed graph $G=(V,E)$ is twinless strongly connected if it contains a strongly connected spanning subgraph without any pair of antiparallel (or twin) edges. The twinless strongly connected components (TSCCs) of a directed graph $G$ are its maximal twinless strongly connected subgraphs. These concepts have several diverse applications, such as the design of telecommunication networks and the structural stability of buildings. A vertex $v \in V$ is a twinless strong articulation point of $G$ if the deletion of $v$ increases the number of TSCCs of $G$. Here, we present the first linear-time algorithm that finds all the twinless strong articulation points of a directed graph. We show that the computation of twinless strong articulation points reduces to the following problem in undirected graphs, which may be of independent interest: Given a $2$-vertex-connected (biconnected) undirected graph $H$, find all vertices $v$ that belong to a vertex-edge cut-pair, i.e., for which there exists an edge $e$ such that $H \setminus \{v,e\}$ is not connected. We develop a linear-time algorithm that not only finds all such vertices $v$, but also computes the number of edges $e$ such that $H \setminus \{v,e\}$ is not connected. This also implies that for each twinless strong articulation point $v$ which is not a strong articulation point in a strongly connected digraph $G$, we can compute the number of TSCCs in $G \setminus v$. We note that the problem of computing all vertices that belong to a vertex-edge cut-pair can be solved in linear-time by exploiting the structure of $3$-vertex-connected (triconnected) components of $H$, represented by an SPQR tree of $H$. Our approach, however, is conceptually simple, and thus likely to be more amenable to practical implementations.