A fast and simple randomized parallel algorithm for the maximal independent set problem
Journal of Algorithms
A simple parallel algorithm for the maximal independent set problem
STOC '85 Proceedings of the seventeenth annual ACM symposium on Theory of computing
Locality in distributed graph algorithms
SIAM Journal on Computing
Lateral Inhibition through Delta-Notch Signaling: A Piecewise Affine Hybrid Model
HSCC '01 Proceedings of the 4th International Workshop on Hybrid Systems: Computation and Control
Maximal independent sets in radio networks
Proceedings of the twenty-fourth annual ACM symposium on Principles of distributed computing
The price of being near-sighted
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Beeping a maximal independent set
DISC'11 Proceedings of the 25th international conference on Distributed computing
Fast deterministic distributed maximal independent set computation on growth-bounded graphs
DISC'05 Proceedings of the 19th international conference on Distributed Computing
| Hi-index | 0.00 |
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 global probability values are chosen. However, we then propose a new algorithm that incorporates another important feature of the biological system: the probability value at each node is adapted using 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. The new algorithm also has only a constant message complexity per node.