arXiv:2606.18106v1 Announce Type: new Abstract: This paper explores the problem of finding the minimum zero-forcing set on undirected graphs and proposes an adapted machine-learning framework to solve the problem. The minimum zero-forcing set problem is a graph coloring problem where the color of an initial set of nodes propagates throughout a network. The set of nodes is zero-forcing if it forces all uncolored nodes to change color under the constraint of the color-change rule. There are several applications to this problem across different domains such as network science, network control, an
The increasing complexity of network problems, coupled with advances in deep reinforcement learning techniques, creates a timely opportunity for novel computational approaches.
Improving the efficiency of solving complex graph problems, such as minimum zero-forcing sets, has implications for optimizing network control and understanding information propagation.
This research introduces a machine learning framework that could enable more effective and automated solutions for previously intractable graph-coloring and network optimization challenges.
- · AI researchers
- · Network scientists
- · Cybersecurity
- · Supply chain logistics
More efficient algorithms for complex network analysis and optimization become available.
Improved resilience and control mechanisms for large-scale infrastructure networks could be developed.
Automated network design and self-healing systems may emerge, reducing manual intervention and operational costs.
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