### Abstract

Language | English |
---|---|

Title of host publication | Structural, Syntactic, and Statistical Pattern Recognition |

Publisher | Springer |

Pages | 43-59 |

Number of pages | 17 |

Volume | 6218 |

ISBN (Print) | 978-3-642-14979-5 |

DOIs | |

Publication status | Published - 28 Aug 2010 |

### Publication series

Name | Lecture Notes in Computer Science |
---|---|

Publisher | Springer |

Volume | 6218 |

### Fingerprint

### Keywords

- subgraph centrality
- Estrada index
- network communities
- communicability

### Cite this

*Structural, Syntactic, and Statistical Pattern Recognition*(Vol. 6218, pp. 43-59). (Lecture Notes in Computer Science; Vol. 6218). Springer. https://doi.org/10.1007/978-3-642-14980-1_4

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*Structural, Syntactic, and Statistical Pattern Recognition.*vol. 6218, Lecture Notes in Computer Science, vol. 6218, Springer, pp. 43-59. https://doi.org/10.1007/978-3-642-14980-1_4

**Structural patterns in complex networks through spectral analysis.** / Estrada, Ernesto.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

TY - GEN

T1 - Structural patterns in complex networks through spectral analysis

AU - Estrada, Ernesto

PY - 2010/8/28

Y1 - 2010/8/28

N2 - The study of some structural properties of networks is introduced from a graph spectral perspective. First, subgraph centrality of nodes is defined and used to classify essential proteins in a proteomic map. This index is then used to produce a method that allows the identification of superhomogeneous networks. At the same time this method classify non-homogeneous network into three universal classes of structure. We give examples of these classes from networks in different real-world scenarios. Finally, a communicability function is studied and showed as an alternative for defining communities in complex networks. Using this approach a community is unambiguously defined and an algorithm for its identification is proposed and exemplified in a real-world network.

AB - The study of some structural properties of networks is introduced from a graph spectral perspective. First, subgraph centrality of nodes is defined and used to classify essential proteins in a proteomic map. This index is then used to produce a method that allows the identification of superhomogeneous networks. At the same time this method classify non-homogeneous network into three universal classes of structure. We give examples of these classes from networks in different real-world scenarios. Finally, a communicability function is studied and showed as an alternative for defining communities in complex networks. Using this approach a community is unambiguously defined and an algorithm for its identification is proposed and exemplified in a real-world network.

KW - subgraph centrality

KW - Estrada index

KW - network communities

KW - communicability

U2 - 10.1007/978-3-642-14980-1_4

DO - 10.1007/978-3-642-14980-1_4

M3 - Conference contribution book

SN - 978-3-642-14979-5

VL - 6218

T3 - Lecture Notes in Computer Science

SP - 43

EP - 59

BT - Structural, Syntactic, and Statistical Pattern Recognition

PB - Springer

ER -