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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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Cite

BibTeXRIS
BibTeX
@misc{deniskin2026convergence,
  title = {{Convergence and acceleration of a nonlinear fixed-point iteration for computing the Fitness Centrality of general graphs}},
  author = {Nikita Deniskin and Michele Benzi},
  howpublished = {arXiv},
  year = {2026},
  month = aug,
  doi = {10.48550/arxiv.2608.28634},
  eprint = {2608.28634},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2608.28634},
}
RIS
TY  - UNPB
TI  - Convergence and acceleration of a nonlinear fixed-point iteration for computing the Fitness Centrality of general graphs
AU  - Nikita Deniskin
AU  - Michele Benzi
PY  - 2026
DA  - 2026/08/08
PB  - arXiv
DO  - 10.48550/arxiv.2608.28634
UR  - https://arxiv.org/abs/2608.28634
AB  - 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.
ER  -