posted on 2022-10-19, 10:17authored byAilbhe Cassidy
We live in a world surrounded by networks. They are ubiquitous. Be it social media
networks linking us to our friends, transportation networks interconnecting locations
around the globe, or the metabolic network breaking down food in our bodies. The
study of networks is an interdisciplinary field spanning from fields of mathematics,
psychology, sociology, biology, computer science, physics, and many other areas. The
field of Network Science has greatly profited from the contributions of such diverse
scientific communities. However, there are still remaining challenges that are open for
further research and discussion. The identification of influential nodes in a network is
a constant challenge faced by researchers. Regardless of the specific field of study the
solution to this problem is constantly in demand.
In this thesis, we present a new measure for the identification of influential nodes in
complex networks. It is based on a mathematical model which uses a branching process
approach. Unlike a lot of existing measures, it is based on a mathematical model that
takes into account not only the structure of the network but also the dynamics taking place
on the network. We present the mathematical theory behind the model and explain from
this where the measure will return accurate results, and when it should return inaccurate
predications. Throughout this work, we provide a considerable amount of results on a
range of networks. We do this to support our proposal and recommendations for the
usage of this new centrality metric.
Funding
Dynamics of the metabolic state in the context of a systematic approach to the study of the processes of growth and development of higher plants and fungi