dorsal/arxiv
View SchemaFinding mesoscopic communities in sparse networks
| Authors | I. Ispolatov, I. Mazo, A. Yuryev |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0512038 |
| URL | https://arxiv.org/abs/q-bio/0512038 |
| DOI | 10.1088/1742-5468/2006/09/P09014 |
| Journal | J. Stat. Mech. (2006) P09014 |
Abstract
We suggest a fast method to find possibly overlapping network communities of a desired size and link density. Our method is a natural generalization of the finite-$T$ superparamegnetic Potts clustering introduced by Blatt, Wiseman, and Domany (Phys. Rev. Lett. v.76, 3251 (1996) and the recently suggested by Reichard and Bornholdt (Phys. Rev. Lett. v.93, 21870 (2004)) annealing of Potts model with global antiferromagnetic term. Similarly to both preceding works, the proposed generalization is based on ordering of ferromagnetic Potts model; the novelty of the proposed approach lies in the adjustable dependence of the antiferromagnetic term on the population of each Potts state, which interpolates between the two previously considered cases. This adjustability allows to empirically tune the algorithm to detect the maximum number of communities of the given size and link density. We illustrate the method by detecting protein complexes in high-throughput protein binding networks.
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"abstract": "We suggest a fast method to find possibly overlapping network communities of\na desired size and link density. Our method is a natural generalization of the\nfinite-$T$ superparamegnetic Potts clustering introduced by Blatt, Wiseman, and\nDomany (Phys. Rev. Lett. v.76, 3251 (1996) and the recently suggested by\nReichard and Bornholdt (Phys. Rev. Lett. v.93, 21870 (2004)) annealing of Potts\nmodel with global antiferromagnetic term. Similarly to both preceding works,\nthe proposed generalization is based on ordering of ferromagnetic Potts model;\nthe novelty of the proposed approach lies in the adjustable dependence of the\nantiferromagnetic term on the population of each Potts state, which\ninterpolates between the two previously considered cases. This adjustability\nallows to empirically tune the algorithm to detect the maximum number of\ncommunities of the given size and link density. We illustrate the method by\ndetecting protein complexes in high-throughput protein binding networks.",
"arxiv_id": "q-bio/0512038",
"authors": [
"I. Ispolatov",
"I. Mazo",
"A. Yuryev"
],
"categories": [
"q-bio.MN",
"cond-mat.dis-nn"
],
"doi": "10.1088/1742-5468/2006/09/P09014",
"journal_ref": "J. Stat. Mech. (2006) P09014",
"title": "Finding mesoscopic communities in sparse networks",
"url": "https://arxiv.org/abs/q-bio/0512038"
},
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