Enhance the Efficiency of Heuristic Algorithm for Maximizing Modularity Q
Yanqing Hu, Jinshan Wu, Zengru Di
TL;DR
This paper proves the equivalence of modularity maximization to a nonconvex quadratic programming problem, enabling more efficient heuristic algorithms for community detection in complex networks, with demonstrated numerical effectiveness.
Contribution
It introduces a novel theoretical equivalence that simplifies and enhances the efficiency of heuristic algorithms for modularity maximization.
Findings
The problem is equivalent to a nonconvex quadratic programming formulation.
The improved heuristic algorithms show significant efficiency gains.
Numerical results confirm the effectiveness of the proposed approach.
Abstract
Modularity Q is an important function for identifying community structure in complex networks. In this paper, we prove that the modularity maximization problem is equivalent to a nonconvex quadratic programming problem. This result provide us a simple way to improve the efficiency of heuristic algorithms for maximizing modularity Q. Many numerical results demonstrate that it is very effective.
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