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arXiv · 2608.28634

Convergence and acceleration of a nonlinear fixed-point iteration for computing the Fitness Centrality of general graphs

Nikita Deniskin·Michele Benzi
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Abstract

We establish the global convergence of the (non-homogeneous) Fitness Centrality algorithm for general graphs, deriving an explicit convergence bound for the corresponding fixed-point iteration. Furthermore, we show how the convergence can be dramatically improved by Anderson acceleration and by switching to Newton's method once a sufficiently good approximation to the fixed point has been found. The efficacy of this strategy is illustrated by numerical experiments on different types of graphs.

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Nikita Deniskin, Michele Benzi. 2026-08-08. Convergence and acceleration of a nonlinear fixed-point iteration for computing the Fitness Centrality of general graphs. https://arxiv.org/abs/2608.28634

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