| Project Description: |
The goal is to study the geometric properties of those network models which contain a spatial component, such as the Spatial Preferential Attachment Model
proposed by W. Aiello, A. Bonato, C. Cooper, J. Janssen and P. Pralat; or the Geographical Threshold Model, which combines random geometric graphs with a threshold for edge formation.
These geometric properties include the distribution of edge lengths and the expected number of common neighbours for vertices located a certain distance apart.
The ultimate goal will be a kind of reverse engineering where if we are given the link structure of data that could be reasonably represented by one of these geometric models,
we will be about to infer a great deal about the underlying geometric structure of the data. In terms of applicability, this tool should allow us to recognize relationships among "close"
nodes better than before, as well as helping with the detection of anomalous nodes.
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