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May 31, 2026MathematicsOpen Access

Redefining Centrality Measures in Weighted Causal Graphs

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Authors

CPCristina PuenteJRJavier RodrigoMLMª Dolores López

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Overview

Randomized trial demonstrates new centrality measures in causal graphs, highlighting their importance for sentence ranking.

Key Points

  • This work aims to enhance the analysis of causal relationships by redefining centrality measures in edge-weighted causal graphs.
  • Introduced new methods to quantify direct and indirect causal effects between sentences.
  • Developed techniques to identify the strongest causal path between two concepts in given contexts.
  • Proposed centrality measures that integrate causality scores and edge weights for ranking sentences by importance.
  • Demonstrated that the new centrality measures effectively rank sentences based on causal significance.
  • Identified pairs of concepts with the strongest causal connections depending on context.
  • Provided methods to quantify the total strength of causal paths between concepts.

Cite This Study

Puente et al. (2026) studied this question.

synapsesocial.com/papers/6a1bd2375783ba022b6fd9cahttps://doi.org/10.3390/math14111887
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