Skip to Main content Skip to Navigation
New interface
Journal articles

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.
Complete list of metadata

Cited literature [36 references]  Display  Hide  Download

https://hal.inria.fr/hal-01291958
Contributor : Dieter Mitsche Connect in order to contact the contributor
Submitted on : Tuesday, March 22, 2016 - 12:51:00 PM
Last modification on : Thursday, August 4, 2022 - 4:54:24 PM
Long-term archiving on: : Sunday, November 13, 2016 - 10:56:12 PM

File

dimensionality.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Anthony Bonato, David Gleich, Myunghwan Kim, Dieter Mitsche, Pawel Pralat, et al.. Dimensionality of Social Networks Using Motifs and Eigenvalues. PLoS ONE, 2014, ⟨10.1371/journal.pone.0106052.s001⟩. ⟨hal-01291958⟩

Share

Metrics

Record views

38

Files downloads

76