Preprint release

Preprint can be found at link

Various improvements. One of the most prominent being:

• Added Potts model
  • With memory dynamics
• Improved settings object

Main release of package

Information impact.

Abstract

One of the most central questions in network science is: which nodes are most important? Often this question is answered using topological properties such as high connectedness or centrality in the network. However it is unclear whether topological connectedness translates directly to dynamical impact. To this end, we simulate the kinetic Ising spin model with weighted edges on generated and a real weighted network. The extent of a node's dynamic impact is assessed causally intervening on a node state and its effect on the systemic dynamics. Here we show that topological features such as network centrality or connectedness are actually poor predictors of the dynamical impact of a node on the rest of the network. A solution through the introduction of a novel measure based on information theory,, information impact, that is able to accurately reflect dynamic importance of nodes in networks under natural dynamics. We conclude that the most dynamically impactful nodes are usually not the most well-connected or central nodes. This implies that the common assumption of topologically central or well-connected nodes being also dynamically important is actually false, and we cannot abstract away the dynamics from a network before analyzing it.

Code release

This is the code release accompanying my master thesis. It has enhanced memory tweaks that should generate to larger graphs, but not immensely large graphs. Code will be updated accordingly.