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Correlated topologies in citation networks and the Web

Abstract
Information networks such as the scientific literature and the Web have been studied extensively by different communities focusing on alternative topological properties induced by citation links, textual content, and semantic relationships. This paper reviews work that brings such different perspectives together in order to build better search tools and to understand how the Web's scale free topology emerges from author behavior. I describe three topologies induced by different classes of similarity measures, and outline empirical data that allows us to quantify and map their correlations. The data is also used to study a power law relationship between the content similarity between two documents and the probability that they are connected by citations or hyperlinks. Such finding has led to a remarkably powerful growth model for information networks, which simultaneously predicts the distribution of degree and the distribution of content similarity across pairs of documents - Web pages connected by links and scientific articles connected by citations.