Work

Redefining Centrality Measures in Weighted Causal Graphs

Year: 2026

Type: article

Abstract: Causal graphs are powerful instruments for structuring information and analyzing the extent to which an observed effect can be attributed to a given cause. In this work, we demonstrate how edge-weighted causal graphs can be used to quantify whether a sentence acts as a direct or indirect cause. We introduce methods to identify the causal path with the highest total strength between two concepts and, given a specific context, to determine the pair of concepts with the strongest causal connection. In addition, we propose centrality measures that incorporate causality scores and graph weights, enabling the ranking of sentences by causal importance and identifying the most relevant ones.

Source: Mathematics

DOI: 10.3390/math14111887

Authors: Cristina Puente, Javier Rodrigo, Mª Dolores López, Álvaro Arrieta-Puente

Institutions: Universidad Pontificia Comillas, Eindhoven University of Technology

Language: English

Topic: Complex Network Analysis Techniques

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: Universidad Pontificia Comillas