2012/11/01 by Alex Scott, Scott, Alex, Peter Jeavons +3 · 1 citation
Computer Science · #Complexity and Algorithms in Graphs #Distributed #Error Correcting Code Techniques #FOS: Computer and information sciences #Optimization and Search Problems #Parallel #and Cluster Computing (cs.DC) #cs.DC
paper · pdf · doi:10.48550/arxiv.1211.0235
arxiv created 2012/11/01 · openalex publication_date 2012/11/01 · arxiv updated 2012/11/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Maximal Independent Set selection is a fundamental problem in distributed computing. A novel probabilistic algorithm for this problem has recently been proposed by Afek et al, inspired by the study of the way that developing cells in the fly become specialised. The algorithm they propose is simple and robust, but not as efficient as previous approaches: the expected time complexity is O(log2 n). Here we first show that the approach of Afek et al cannot achieve better efficiency than this across all networks, no matter how the probability values are chosen. However, we then propose a new algorithm that incorporates another important feature of the biological system: adapting the probabilities used at each node based on local feedback from neighbouring nodes. Our new algorithm retains all the advantages of simplicity and robustness, but also achieves the optimal efficiency of O(log n) expected time.