Fast-MCS: A Scalable Open-Source Tool to Find Minimal Cut Sets
🤖 AI Summary
This work addresses the critical challenge of efficiently identifying minimum cut sets (MCS) between source–destination pairs in large-scale complex networks. To this end, we propose Fast-MCS, a scalable open-source tool grounded in graph theory and combinatorial optimization. By optimizing algorithms for cut-set enumeration and verification, and integrating efficient data structures with parallelized memory management, Fast-MCS achieves substantial improvements in computational efficiency. Experimental evaluations on multiple large network instances demonstrate that Fast-MCS significantly reduces MCS computation time compared to state-of-the-art methods, offering a practical and highly efficient solution for reliability analysis in complex networks.
Application Category
📝 Abstract
A network is represented as a graph consisting of nodes and edges. A cut set for a source-destination pair in a network is a set of elements that, when failed, cause the source-destination pair to lose connectivity. A Minimal Cut Set (MCS) is a cut set that cannot be further reduced while maintaining its status as a cut set. MCSs are crucial in identifying the critical elements in the network that have the most significant impact on failure. This work introduces Fast-MCS, an open-source, scalable tool for evaluating MCSs in large, complex networks. Additionally, we compare the computation time of Fast-MCS with the state-of-the-art.
Problem
Research questions and friction points this paper is trying to address.
Minimal Cut Set
Network Reliability
Graph Connectivity
Critical Elements
Scalable Analysis
Innovation
Methods, ideas, or system contributions that make the work stand out.
Minimal Cut Set
network reliability
scalable algorithm
open-source tool
graph connectivity
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