K-core decomposition of Internet graphs: hierarchies, self-similarity and measurement biases
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1.
CONICET and Facultad de Ingeniería, Universidad de Buenos Aires, Paseo Colón 850, C1063ACV Ciudad de Buenos Aires
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Abdus Salam International Center for Theoretical Physics, Strada Costiera 11, 34014 Trieste
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3.
LPT (UMR du CNRS 8627), Université de Paris-Sud
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4.
School of Informatics, Indiana University, Bloomington, IN 47048
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Received:
01 February 2008
Revised:
01 March 2008
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68R10, 05C90, 68M07.
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We consider the $k$-core decomposition of network models and Internet graphs at the autonomous system (AS) level. The k-core analysis allows to characterize networks beyond the degree distribution and uncover structural properties and hierarchies due to the specific architecture of the system. We compare the $k$-core structure obtained for AS graphs with those of several network models and discuss the differences and similarities with the real Internet architecture. The presence of biases and the incompleteness of the real maps are discussed and their effect on the $k$-core analysis is assessed with numerical experiments simulating biased exploration on a wide range of network models. We find that the $k$-core analysis provides an interesting characterization of the fluctuations and incompleteness of maps as well as information helping to discriminate the original underlying structure.
Citation: José Ignacio Alvarez-Hamelin, Luca Dall'Asta, Alain Barrat, Alessandro Vespignani. K-core decomposition of Internet graphs: hierarchies, self-similarity and measurement biases[J]. Networks and Heterogeneous Media, 2008, 3(2): 371-393. doi: 10.3934/nhm.2008.3.371
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Abstract
We consider the $k$-core decomposition of network models and Internet graphs at the autonomous system (AS) level. The k-core analysis allows to characterize networks beyond the degree distribution and uncover structural properties and hierarchies due to the specific architecture of the system. We compare the $k$-core structure obtained for AS graphs with those of several network models and discuss the differences and similarities with the real Internet architecture. The presence of biases and the incompleteness of the real maps are discussed and their effect on the $k$-core analysis is assessed with numerical experiments simulating biased exploration on a wide range of network models. We find that the $k$-core analysis provides an interesting characterization of the fluctuations and incompleteness of maps as well as information helping to discriminate the original underlying structure.
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