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Journal of Graphics ›› 2026, Vol. 47 ›› Issue (5): 1125-1134.DOI: 10.11996/JG.j.2095-302X.2026051125

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Conflict measurement in complex networks of Yellow River ecological governance based on knowledge graph and R-GAT model

XU Jiaming1,2, CHEN Guangyao1, XU Zhao1()   

  1. 1 School of Civil Engineering, Southeast University, Nanjing Jiangsu 211100, China
    2 School of Civil and Hydraulic Engineering, Lanzhou University of Technology, Lanzhou Gansu 730050, China
  • Received:2026-05-06 Accepted:2026-08-19 Online:2026-10-31 Published:2026-10-26
  • Supported by:
    National Key Research and Development Program of China(2021YFF0500900);Soft Science Special Project of the Gansu Basic Research Plan(26JRZA125)

Abstract:

Ecological governance in the Yellow River Basin involves coordinating multiple agents, tasks, and mechanisms. Multi-source texts including policy documents, corporate reports, and public-opinion texts contain numerous implicit governance relationships and clues regarding conflict transmission. Converting this information into computable graph structures to identify and measure conflicts represents a key challenge in complex network governance analysis. To address the limitations of existing methods in organizing unstructured governance semantics, expressing conflict topology, and quantifying conflict intensity, a complex network conflict measurement method integrating knowledge graphs with Relational Graph Attention Networks (R-GAT) was proposed, drawing on approaches to complex-object modeling, topological relationship representation, and visual analysis in graphics. The semantic structures and directed relational networks provided by knowledge graphs were used as structural foundations and semantic priors for R-GAT, through which, through which the impact intensity of different relational edges on conflict formation, were learned and complex governance relationships were transformed from textual semantics into graph structures, topological analysis, and quantitative measurement. Experimental results demonstrated favorable performance in ranking conflict intensity and identifying strong conflict relationships, and key conflict relationships within the complex network of Yellow River ecological governance were effectively identified. A knowledge-graph-driven graph-learning method was thereby provided for graph-structural modeling of complex relational objects and multi-agent governance conflict measurement.

Key words: complex network, polycentric ecological governance, knowledge graph, conflict measurement

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