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)
SUMMARY Analysis of crime data is crucial for prevention and assessment of illegal activity. This paper is one of the first case studies of a crime dataset collected in New South Wales, Australia. We apply methods from complex network analysis to identify key aspects of criminal activity in the state of New South Wales. We further detect groups of local(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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