NuCDS: An Efficient Local Search Algorithm for Minimum Connected Dominating Set

NuCDS: An Efficient Local Search Algorithm for Minimum Connected Dominating Set

Bohan Li, Xindi Zhang, Shaowei Cai, Jinkun Lin, Yiyuan Wang, Christian Blum

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 1503-1510. https://doi.org/10.24963/ijcai.2020/209

The minimum connected dominating set (MCDS) problem is an important extension of the minimum dominating set problem, with wide applications, especially in wireless networks. Despite its practical importance, there are few works on solving MCDS for massive graphs, mainly due to the complexity of maintaining connectivity. In this paper, we propose two novel ideas, and develop a new local search algorithm for MCDS called NuCDS. First, a hybrid dynamic connectivity maintenance method is designed to switch alternately between a novel fast connectivity maintenance method based on spanning tree and its previous counterpart. Second, we define a new vertex property called \emph{safety} to make the algorithm more considerate when selecting vertices. Experiments show that NuCDS significantly outperforms the state-of-the-art MCDS algorithms on both massive graphs and classic benchmarks.
Keywords:
Heuristic Search and Game Playing: Combinatorial Search and Optimisation
Heuristic Search and Game Playing: Heuristic Search