1. Generative Model To Construct Blog and Post Networks In Blogosphere
Amit Karandikar, Akshay Java, Anupam Joshi, Tim Finin
AIM To simulate the graph structures that look like the “real” Blogosphere
Graphs are everywhere ..
Ok, but why? To test the new algorithms for blogosphere, To save time and effort in gathering and preprocessing the "real" data, To extrapolate the properties by varying parameters
.. and so are Power Laws!
20% of the population owns 80% of the wealth Real networks often tend to show the “rich get richer” phenomenon. Already popular website is bound to get more inlinks.
Blogger characteristics
Blog writers are enthusiastic blog readers
Most bloggers post infrequently
Active bloggers follow popular blogs, friends blogs and interact online.
Scale-free networks
The likelihood of linking to a popular website is higher
Ok, power laws:
So what’s the big deal?
Makes ranking of web content possible
(e.g. Google ranking)
Shows that only a few things are more important than others
Preferential Attachment
Approach
Model bloggers with Read, Write, Idle states
Select blog writers preferentially based on the outdegree of the blog node
Perform preferential random walk in the blog neighborhood based on the indegree of the neighbor
With a small probability perform totally random reading and writer selection
Conclusion
We were able to simulate the blogosphere properties such as degree distributions, average shortest path, diameter, degree correlations, reciprocity, size of connected components