The study of complex networks has received much attention over the past few decades, presenting a simple, yet efficient means of modelling and understanding complex systems. The majority of network science literature focuses on simple one-mode networks. In the real world, however, we often find systems that are best represented by bipartite networks that are commonly analysed by examination of their one-mode projection. One-mode projections are naturally very dense and noisy networks and hence the most relevant information may be hidden. One way to reveal hidden information is the extraction of significant edges, forming the backbone of the projection. Existing methods are computationally expensive. In this talk, I will introduce a computationally inexpensive method for extracting the backbone of projected bipartite networks. I will demonstrate that the edge weights of projections follow a Poisson binomial distribution and that finding the expected weight distribution of a random bipartite projection only requires knowledge of the bipartite degree distributions.

About the speaker: Jessica Liebig received her PhD in January 2017 from RMIT University. Her primary research interest lies in the area of network science and is directed toward the study of large, complex bipartite data. The work presented in her thesis examines bipartite networks with the aim of uncovering significant behaviour in real world networks.

How to participate in this seminar:

1. Book your nearest ACE facility;

2. Notify Vera Roshchina at RMIT ( to notify you will be participating.

No access to an ACE facility? Contact Maaike Wienk to arrange a temporary Visimeet licence for remote access (limited number of licences available – first come first serve)

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