Co-clustering documents and words using bipartite spectral graph partitioning
From Cohen Courses
Contents
Citation
M.E.J.Newman. 2001. The Structure of Scientific Collaboration Networks. Proceedings of the National Academy of Sciences. 404-409.
Online Version
http://www.cs.utexas.edu/users/inderjit/public_papers/kdd_bipartite.pdf
Databases
MEDLINE (biomedical research)[1]
Los Alamos e-Print Archive (physics)[2]
Summary
This is a paper investigating the structure of scientific collaboration. The author ulitized data from a number of databases in different fields: Biomedical, Physics and Computer Science. Properties of these networks are:
- In all cases, scientific communities seem to constitute a ‘‘small world,’’[4] in which the average distance between scientists via a line of intermediate collaborators varies logarithmically with the size of the relevant community.
- Those networks are highly clustered, meaning that two scientists are much more likely to have collaborated if they have a third common collaborator than are two scientists chosen at random from the community.
- Distributions of both the number of collaborators of scientists and the numbers of papers are well fit by power-law forms with an exponential cutoff. This cutoff may be caused by the finite time window (1995-1999) used in the study.
- There are a number of significant statistical differences between different scientific communities. Some of these are obvious.
