Network Interdiction Goes Neural

• URL: http://arxiv.org/abs/2405.16409v1
• Date: Sun, 26 May 2024 02:34:26 GMT
• Title: Network Interdiction Goes Neural
• Authors: Lei Zhang, Zhiqian Chen, Chang-Tien Lu, Liang Zhao,
• Abstract summary: Network interdiction problems are optimization problems involving two players. One aims to solve an optimization problem on a network, while the other seeks to modify the network to thwart the first player's objectives.
• Score: 26.308933674471895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Network interdiction problems are combinatorial optimization problems involving two players: one aims to solve an optimization problem on a network, while the other seeks to modify the network to thwart the first player's objectives. Such problems typically emerge in an attacker-defender context, encompassing areas such as military operations, disease spread analysis, and communication network management. The primary bottleneck in network interdiction arises from the high time complexity of using conventional exact solvers and the challenges associated with devising efficient heuristic solvers. GNNs, recognized as a cutting-edge methodology, have shown significant effectiveness in addressing single-level CO problems on graphs, such as the traveling salesman problem, graph matching, and graph edit distance. Nevertheless, network interdiction presents a bi-level optimization challenge, which current GNNs find difficult to manage. To address this gap, we represent network interdiction problems as Mixed-Integer Linear Programming (MILP) instances, then apply a multipartite GNN with sufficient representational capacity to learn these formulations. This approach ensures that our neural network is more compatible with the mathematical algorithms designed to solve network interdiction problems, resulting in improved generalization. Through two distinct tasks, we demonstrate that our proposed method outperforms theoretical baseline models and provides advantages over traditional exact solvers.

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