Dynamical importance of nodes is poorly predicted by static topological features
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2019-03-01
Language
en
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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 on generated and a real-world network with weighted edges. The extent of the dynamic impact
is assessed by causally intervening on a node state and effect on the systemic dynamics. The results 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 is offered in the form of an information theoretical measure named
information impact. The metric is able to accurately reflect dynamic importance of nodes in networks under natural
dynamics using observations only, and validated using causal interventions. 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.
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Faculteit der Sociale Wetenschappen