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Dimensionality of social networks using motifs and eigenvalues.


ABSTRACT: We consider the dimensionality of social networks, and develop experiments aimed at predicting that dimension. We find that a social network model with nodes and links sampled from an m-dimensional metric space with power-law distributed influence regions best fits samples from real-world networks when m scales logarithmically with the number of nodes of the network. This supports a logarithmic dimension hypothesis, and we provide evidence with two different social networks, Facebook and LinkedIn. Further, we employ two different methods for confirming the hypothesis: the first uses the distribution of motif counts, and the second exploits the eigenvalue distribution.

SUBMITTER: Bonato A 

PROVIDER: S-EPMC4154874 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Dimensionality of social networks using motifs and eigenvalues.

Bonato Anthony A   Gleich David F DF   Kim Myunghwan M   Mitsche Dieter D   Prałat Paweł P   Tian Yanhua Y   Young Stephen J SJ  

PloS one 20140904 9


We consider the dimensionality of social networks, and develop experiments aimed at predicting that dimension. We find that a social network model with nodes and links sampled from an m-dimensional metric space with power-law distributed influence regions best fits samples from real-world networks when m scales logarithmically with the number of nodes of the network. This supports a logarithmic dimension hypothesis, and we provide evidence with two different social networks, Facebook and LinkedI  ...[more]

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