Analysis of Blog graph by CMU researchers : Interesting observations
Temporal patterns: For the two
months of observation,we found that blog posts do not have a bursty
behavior;they only have a weekly periodicity. Most surprisingly,the
popularity of posts drops with a power law, instead of exponentially,
that one may have expected. Surprisingly, the exponent of the power law
is -1.5, agreeing very well with Barabasi’s theory of heavy tails in
human behavior .
Patterns in the shapes and sizes of cascades and blogs: Almost
every metric we measured, followed a power law. The most striking
result is that the size distribution of cascades (= number of involved
posts), follows a perfect Zipfian distribution, that is, a power law
with slope =-2. The other striking discovery was on the shape of
cascades. The most popular shapes were the “stars”, that is, a single
post with several in-links, but none of the citing posts are themselves
cited.
Generating Model: Finally, we
design a flu-like epidemiological model. Despite its simplicity, it
generates cascades that match several of the above power-law properties
of real cascades. This model could be useful for link prediction,
link-spam detection, and “what-if” scenarios
Link : www.cs.cmu.edu/~jure/pubs/blogs-sdm07.pdf