The likelihood of linking to a popular website is higher Second Space: A Generative Model For The Blogosphere Amit Karandikar, Akshay Java, Anupam Joshi, Tim Finin, Yelena Yesha, and Yaacov Yesha Goal: generate graph structures better approximating those found on the Blogosphere Motivation: To test the new algorithms for Blogosphere, save time and effort in gathering and preprocessing the "real" data, extrapolate the properties by varying parameters Graphs are everywhere .. .. 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. Idea: simulate characteristics of Blogging agents Blog writers are enthusiastic blog readers Most bloggers post infrequently Active bloggers follow popular blogs, friend’s blogs and interact online. Scale-free networks 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 The likelihood of linking to a popular website is higher Preferential Attachment Approach Model bloggers with read, write and 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 neighbor’s indegree With a small probability perform totally random reading and writer selection Conclusion We better approximated Blogosphere graph properties including degree distributions, average shortest path, diameter, degree correlations, reciprocity, size of connected components