Influential Yield Strength of Steel Materials with Return Random Walk Gravity Centrality

Rocío Rodríguez (), Manuel Curado, Francy D. Rodríguez and José F. Vicent
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Rocío Rodríguez: Tidop Research Group, University of Salamanca, Patio de Escuelas 1, E-38008 Salamanca, Spain
Manuel Curado: Department of Computer Science and Artificial Intelligence, University of Alicante, Campus de San Vicente, Ap. Correos 99, E-03080 Alicante, Spain
Francy D. Rodríguez: Department of Computer, Catholic University of Ãvila, Calle Canteros, s/n, E-05005 Ãvila, Spain
José F. Vicent: Department of Computer Science and Artificial Intelligence, University of Alicante, Campus de San Vicente, Ap. Correos 99, E-03080 Alicante, Spain

Mathematics, 2024, vol. 12, issue 3, 1-12

Abstract: In complex networks, important nodes have a significant impact, both functional and structural. From the perspective of data flow pattern detection, the evaluation of the importance of a node in a network, taking into account the role it plays as a transition element in random paths between two other nodes, has important applications in many areas. Advances in complex networks and improved data generation are very important for the growth of computational materials science. The search for patterns of behavior of the elements that make up steels through complex networks can be very useful in understanding their mechanical properties. This work aims to study the influence of the connections between the elements of steel and the impact of these connections on their mechanical properties, more specifically on the yield strength. The patterns found in the results show the significance of the proposed approach for the development of new steel compositions.

Keywords: centrality measure; complex networks; random walk; steel materials (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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