2014/05/01 by Anthony Bonato, David F. Gleich, Myunghwan Kim +4 · 1 citation
Computer Science · Physics and Astronomy · #cs.SI #physics.soc-ph
paper · pdf · doi:10.1371/journal.pone.0106052
26 pages
arxiv created 2014/05/01 · arxiv updated 2015/06/19
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.