Application of semidefinite programming to maximize the spectral gap produced by node removal

Naoki Masuda; Tetsuya Fujie; Kazuo Murota

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arXiv · 1301.1503

Application of semidefinite programming to maximize the spectral gap produced by node removal

Naoki Masuda·Tetsuya Fujie·Kazuo Murota

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Abstract

The smallest positive eigenvalue of the Laplacian of a network is called the spectral gap and characterizes various dynamics on networks. We propose mathematical programming methods to maximize the spectral gap of a given network by removing a fixed number of nodes. We formulate relaxed versions of the original problem using semidefinite programming and apply them to example networks.

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Naoki Masuda, Tetsuya Fujie, Kazuo Murota. 2013-01-08. Application of semidefinite programming to maximize the spectral gap produced by node removal. https://arxiv.org/abs/1301.1503

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