Jessica Liebig

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Bipartite networks have gained an increasing amount of attention over the past few years. Network measures in particular, have been the focus of this research as many of them cannot be directly applied to bipartite networks. The clustering coefficient is one measure that has been redefined recently to suit the analysis of bipartite networks. Building up on(More)
The analysis of crime datasets is necessary in order to prevent and assess criminal activity [17]. Information about different types of crimes can often be found in the form of annual reports published by government bodies, but rarely in the form of publicly available datasets that may be used for research. In contrast, the New South Wales Bureau of Crime(More)
This paper introduces a computationally inexpensive method of extracting the backbone of one-mode networks projected from bipartite networks. We show that the edge weights in one-mode projections are distributed according to a Poisson binomial distribution. Finding the expected weight distribution of a one-mode network projected from a random bipartite(More)
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