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- Title
Dimensionality of Social Networks Using Motifs and Eigenvalues.
- Authors
Bonato, Anthony; Gleich, David F.; Kim, Myunghwan; Mitsche, Dieter; Prałat, Paweł; Tian, Yanhua; Young, Stephen J.
- 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.
- Subjects
SOCIAL network analysis; EIGENVALUES; PREDICTION theory; FACEBOOK (Web resource); LINKEDIN (Web resource); NETWORK analysis (Communication)
- Publication
PLoS ONE, 2014, Vol 9, Issue 9, p1
- ISSN
1932-6203
- Publication type
Article
- DOI
10.1371/journal.pone.0106052