English

Ground state energy of $q$-state Potts model: the minimum modularity

Statistical Mechanics 2015-06-19 v1

Abstract

A wide range of interacting systems can be described by complex networks. A common feature of such networks is that they consist of several communities or modules, the degree of which may quantified as the \emph{modularity}. However, even a random uncorrelated network, which has no obvious modular structure, has a finite modularity due to the quenched disorder. For this reason, the modularity of a given network is meaningful only when it is compared with that of a randomized network with the same degree distribution. In this context, it is important to calculate the modularity of a random uncorrelated network with an arbitrary degree distribution. The modularity of a random network has been calculated [Phys. Rev. E \textbf{76}, 015102 (2007)]; however, this was limited to the case whereby the network was assumed to have only two communities, and it is evident that the modularity should be calculated in general with q(2)q(\geq 2) communities. Here, we calculate the modularity for qq communities by evaluating the ground state energy of the qq-state Potts Hamiltonian, based on replica symmetric solutions assuming that the mean degree is large. We found that the modularity is proportional to k/k\langle \sqrt{k} \rangle / \langle k \rangle regardless of qq and that only the coefficient depends on qq. In particular, when the degree distribution follows a power law, the modularity is proportional to k1/2\langle k \rangle^{-1/2}. Our analytical results are confirmed by comparison with numerical simulations. Therefore, our results can be used as reference values for real-world networks.

Keywords

Cite

@article{arxiv.1403.5847,
  title  = {Ground state energy of $q$-state Potts model: the minimum modularity},
  author = {Jae Sung Lee and Sungmin Hwang and Joonhyun Yeo and Doochul Kim and Byungnam Kahng},
  journal= {arXiv preprint arXiv:1403.5847},
  year   = {2015}
}

Comments

14 pages, 4 figures