Computing the Center of Uncertain Points on Cactus Graphs
Ran Hu, Divy H. Kanani, Jingru Zhang
TL;DR
This paper addresses the weighted one-center problem for n uncertain points on a cactus graph, where each uncertain point has m possible locations with probabilities. It introduces a linear-time reduction to a vertex-constrained instance and leverages a skeleton tree decomposition of the cactus to guide a binary-search-like center localization, resolving a center-detecting subproblem on blocks, including a cycle case handled by dynamic convex hulls. The main result is an O(|G| + mn log mn) time algorithm, near-optimal given the input size, which extends prior tree-centric and path-centric results to cactus graphs. The approach provides a general framework for efficient center computation under uncertainty on complex graph families and suggests directions for further optimization of cycle subproblems and broader uncertain-graph center problems.
Abstract
In this paper, we consider the (weighted) one-center problem of uncertain points on a cactus graph. Given are a cactus graph $G$ and a set of $n$ uncertain points. Each uncertain point has $m$ possible locations on $G$ with probabilities and a non-negative weight. The (weighted) one-center problem aims to compute a point (the center) $x^*$ on $G$ to minimize the maximum (weighted) expected distance from $x^*$ to all uncertain points. No previous algorithm is known for this problem. In this paper, we propose an $O(|G| + mn\log mn)$-time algorithm for solving it. Since the input is $O(|G|+mn)$, our algorithm is almost optimal.
