# Community Structure Detection for Directed Networks through Modularity Optimisation

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## Abstract

**:**

## 1. Introduction

## 2. Iterative Mathematical Programming Model for Modularity Optimisation on Directed Networks

Sets | |

$n,e$ | node |

m | module |

${l}_{ne}$ | directed edge pointing from node n to e |

Parameters | |

${\beta}_{ne}$ | weight of edge point from node n to e |

${d}_{n}^{in}$ | sum of weights over all edges points to node n; incoming edge weight |

${d}_{n}^{out}$ | sum of weights over all edges points from node n; outgoing edge weight |

L | total amount of weights over all edges in the given network |

Binary Variables | |

${Y}_{nm}$ | 1 if node n belongs to module m; 0 otherwise |

Free Variables | |

${D}_{m}^{in}$ | sum of ${d}_{n}^{in}$ for all nodes that belong to module m (${Y}_{nm}=1$) |

${D}_{m}^{out}$ | sum of ${d}_{n}^{out}$ for all nodes that belong to module m (${Y}_{nm}=1$) |

${L}_{m}$ | sum of edge weights in module m |

$L{S}_{nem}$ | a positive intermediate variable. $L{S}_{nem}={\beta}_{ne}$ if both nodes n to e belong to module m; 0 otherwise |

${D}_{m}^{in}{Y}_{nm}$ | represent the product of ${D}_{m}^{in}$ and ${Y}_{nm}$, used as an intermediate variable for the MIP model |

$D{D}_{m}^{in\_out}$ | represent the product of ${D}_{m}^{in}$ and ${D}_{m}^{out}$, used as an intermediate variable for the MIP model |

#### 2.1. First Model—MINLP

#### 2.2. Second Model—MIP

#### 2.3. Full Algorithm

Algorithm 1: Our proposed algorithm DiMod for detecting modules in directed networks. |

## 3. Results

#### 3.1. Synthetic Networks

#### 3.2. Real Networks

## 4. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## Abbreviations

MINLP | Mixed Integer Non-Linear Programming |

MIP | Mixed Integer Linear Programming |

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Nodes | Edges | Type of Network | |
---|---|---|---|

Mycobacterium tuberculosis | 194 | 849 | unweighted |

Caenorhabditis elegans | 297 | 2345 | weighted |

Roget’s thesaurus | 994 | 5058 | unweighted |

Plasmodium falciparum | 1390 | 6497 | unweighted |

gnutella08 | 6301 | 20,777 | unweighted |

**Table 2.**Modularity improvement achieved by second step of the proposed method over the initial division network given by the MINLP.

Myc. tub. | C. elegans | Roget | P. falc. | gnutella08 | |
---|---|---|---|---|---|

Initial modularity | 0.4636 | 0.4877 | 0.5063 | 0.6978 | 0.4333 |

Final modularity | 0.5073 | 0.5076 | 0.5860 | 0.7238 | 0.4678 |

Improvement | 9.43% | 4.08% | 15.75% | 3.72% | 7.97% |

Number of modules | 9 | 5 | 13 | 20 | 24 |

Myc. tub. | C. elegans | Roget | P. falc. | gnutella08 | |
---|---|---|---|---|---|

Extremal | 0.4802 | 0.4731 | 0.5582 | 0.6685 | 0.2475 |

Fast algorithm | 0.4567 | 0.5058 | 0.5002 | 0.6846 | 0.4624 |

Tabu search | 0.4635 | 0.4438 | 0.5021 | 0.6496 | 0.2281 |

DiMod | 0.5073 | 0.5076 | 0.5860 | 0.7238 | 0.4678 |

© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

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**MDPI and ACS Style**

Yang, L.; Silva, J.C.; Papageorgiou, L.G.; Tsoka, S. Community Structure Detection for Directed Networks through Modularity Optimisation. *Algorithms* **2016**, *9*, 73.
https://doi.org/10.3390/a9040073

**AMA Style**

Yang L, Silva JC, Papageorgiou LG, Tsoka S. Community Structure Detection for Directed Networks through Modularity Optimisation. *Algorithms*. 2016; 9(4):73.
https://doi.org/10.3390/a9040073

**Chicago/Turabian Style**

Yang, Lingjian, Jonathan C. Silva, Lazaros G. Papageorgiou, and Sophia Tsoka. 2016. "Community Structure Detection for Directed Networks through Modularity Optimisation" *Algorithms* 9, no. 4: 73.
https://doi.org/10.3390/a9040073