  Document networks are characteristic in that a document node, e.g. a webpage
or an article, carries meaningful content. Properties of document networks are
not only affected by topological connectivity between nodes, but also strongly
influenced by the semantic relation between content of the nodes. We observe
that document networks have a large number of triangles and a high value of
clustering coefficient. And there is a strong correlation between the
probability of formation of a triangle and the content similarity among the
three nodes involved. We propose the degree-similarity product (DSP) model
which well reproduces these properties. The model achieves this by using a
preferential attachment mechanism which favours the linkage between nodes that
are both popular and similar. This work is a step forward towards a better
understanding of the structure and evolution of document networks.
