Application of semidefinite programming to maximize the spectral gap produced by node removal
Disordered Systems and Neural Networks
2013-01-09 v1 Optimization and Control
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.
Cite
@article{arxiv.1301.1503,
title = {Application of semidefinite programming to maximize the spectral gap produced by node removal},
author = {Naoki Masuda and Tetsuya Fujie and Kazuo Murota},
journal= {arXiv preprint arXiv:1301.1503},
year = {2013}
}
Comments
1 figure. Short paper presented in CompleNet, Berlin, March 13-15 (2013)