Properties of Random Network Models

Many existing random network models such as Erdős–Rényi and preferential attachment have a few deficiencies in modelling real-world networks. In particular, real -world networks are often highly clustered while the models fail to explain that property. The project aims to develop and study a new general embellishment of network models that rectifies some of those deficiencies, through the random addition of edges between vertices that have common connections. We will also assess the performance of the new embellishment by comparing the predictions of embellished models to real-world networks.

David Chen

The University of Melbourne

David Chen is a student at the University of Melbourne. His undergraduate studies with major in mathematics and statistics covers multiple disciplines including pure and applied mathematics, computing, finance, probability and statistics. David completed a project on character theory in 2021, and he is currently exploring his interests in stochastic models and statistics. He is also interested in programming and algorithms and their applications in mathematical research. He wishes to continue his study in mathematics through a Masters program.

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