Title: Comparative Network Analysis
Speaker: Sumeet Agarwal
Abstract:
Many real-world systems are naturally represented as networks, and a
diverse range of measures have been developed to characterise their
structure. However, studies of networks typically employ only small,
partly arbitrarily selected subsets of these, and the lack of a
comparison makes it unclear which structural diagnostics are redundant
or complementary. We present a highly comparative study of networks and
network features, analysing a wide variety of networks, derived both
from empirical obser- vation and from mathematical models. We collate
and examine a total of over four hundred network diagnostics or summary
statistics thereof. We demonstrate how our approach can be used to
organise and classify net- works, as well as to obtain insights into how
network structure relates to functionally relevant characteristics in a
variety of settings. These include finding fast estimators for the
solution of hard graph problems, detecting structural features of
metabolic networks that correlate with biological evolution, and
constructing summary statistics that allow for efficient fitting of
generative models to observed networks, via an Approximate Bayesian
algorithm. Our methodology provides a general data-driven ap- proach to
aid the study and understanding of networks.