Dimensionality of Social Networks Using Motifs and EigenvaluesReport as inadecuate




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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.



Author: Anthony Bonato , David F. Gleich , Myunghwan Kim, Dieter Mitsche, Paweł Prałat, Yanhua Tian, Stephen J. Young

Source: http://plos.srce.hr/



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