UMBC ebiquity
Masters Thesis
Generative Model To Construct Blog and Post Networks In Blogosphere (Masters Thesis Presentation)
May 1, 2007
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Web graphs have been very useful in the structural and statistical analysis of the web. Various models have been proposed to simulate web graphs that generate degree distributions similar to the web. Real world blog networks resemble many properties of web graphs. But the dynamic nature of the blogosphere and the link structure evolving due to blog readership and social interactions is not well expressed by the existing models. In this research we propose a model for a blogger to construct blog graphs. We combine the existing preferential attachment and random attachment model to generate blog graphs which are type of scale-free networks. The blogger is modeled using read, write, idle states and finite read memory. The combination of these techniques helps in evolution of time stamped blog-blog and post-post network through citations within the blog-blog network. Other parameters like the growth function and the randomness in reading and writing posts help in the formation of graphs with different structural properties. We empirically show that these simulated blog graph exhibits properties similar to the real world blog networks in their degree distributions, degree correlations and clustering coefficient. We believe that this model will help researchers to evaluate and analyze the properties of the blogosphere and facilitate the testing of new algorithms.
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