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M-Fibration Theory with Applications to Weighted Graphs

arXiv 2608.25598 ↗First posted 26 Aug 2026cs.LGcited by 0

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Abstract (arXiv)

The purpose of this paper is to provide a general, comprehensive, theoretical framework that allows one to deal with fibrations on graphs labelled on a commutative monoid. This is a genuine extension of the theory of graph fibrations (as introduced in "Fibrations of Graphs" [Discrete Math., vol. 243, pp. 21-66, 2002]), that makes it possible to deal with weighted graphs, and also graphs labelled with other algebraic structures. The derived theory also lends itself naturally to consider approximate fibrations. As an example, we show how the derived theory can be applied to the reduction of weighted networks, providing a strong theoretical underpinning to recent empirical results.

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References · 6

  1. 2018How Powerful are Graph Neural Networks?cited by 4,393
  2. 2018Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networkscited by 881
  3. 2015Tight Lower and Upper Bounds for the Complexity of Canonical Colour Refinementcited by 16
  4. 2013Dimension Reduction via Colour Refinementcited by 6
  5. 2024The Role of Fibration Symmetries in Geometric Deep Learningcited by 2
  6. 2022Optimal Computation in Anonymous Dynamic Networkscited by 1

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