Exploring the origins of structural diversity in complex networks

What is it about?

Networks of complex systems across applied disciplines display non-trivial, diverse properties in their structure. A convergence in the field of network science is beginning to emerge in which we understand that two main components of network structure are heavy-tailed node fitness and a latent node similarity space. A good illustration of these in a social network would be charisma as a node fitness function and common interests making up a similarity space. So, two people are most likely to share a connection if they are both charismatic and share many common interests, while strong charisma can make up for lack of common interests and vice versa. While networks display diverse patterns, we demonstrate that, in fact, the diversity of structure found within these networks can be explained just by these two properties combining together using our own proposed normalised measurement of statistical complexity.

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Photo by Koushik Chowdavarapu on Unsplash

Photo by Koushik Chowdavarapu on Unsplash

Why is it important?

This work deepens our understanding of the generative principles at play in the interdependencies of complex systems, and how some fairly simple properties of these systems combine to give rise to highly diverse patterns.

Perspectives