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Comparing networks using subgraph counts

Prof. Charlotte Deane ( Department of Statistics, University of Oxford )

Data in many areas of science and sociology is now routinely represented in the form of networks. A fundamental task often required is to compare two datasets (networks) to assess the level of similarity between them. In the context of biological sciences, networks often represent either direct or indirect molecular interactions and an active research area is to assess the level of conservation of interaction patterns across species.

Currently biological network comparison software largely relies on the concept of alignment where close matches between the nodes of two or more networks are sought. These node matches are based on sequence similarity and/or interaction patterns. However, because of the incomplete and error-prone datasets currently available, such methods have had limited success. Moreover, the results of network alignment are in general not amenable for distance-based evolutionary analysis of sets of networks. In this talk I will describe Netdis, a topology-based distance measure between networks, which offers the possibility of network phylogeny reconstruction.



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