基于知识图谱及R-GAT模型的黄河生态治理复杂网络冲突测度

doi:10.11996/JG.j.2095-302X.2026051125

摘要/Abstract

摘要：

黄河流域生态治理涉及多主体、多任务与多机制的协同，政策文件、企业报告及社会舆情等多源文本中蕴含大量隐性治理关系与冲突传导线索。如何将这些信息转化为可计算的图结构，并实现冲突的识别与测度，是复杂网络治理分析中的关键问题。针对现有方法在非结构化治理语义组织、冲突关系拓扑表达及冲突强度量化方面的不足，借鉴图学领域中复杂对象建模、拓扑关系表达与可视分析的思路，提出了一种融合知识图谱与关系型图注意力网络（R-GAT）的黄河生态治理复杂网络冲突测度方法。该方法以知识图谱提供的语义结构和有向关系网络作为R-GAT的结构基础与语义先验，学习不同关系边在冲突形成中的作用强度，实现复杂治理关系从文本语义到图结构表达、拓扑分析及量化测度的转化。实验结果表明，该方法在冲突强弱排序与强冲突关系判别上表现较优，能够有效识别黄河生态治理复杂网络中的关键冲突关系，为复杂关系对象的图结构建模与多主体治理冲突测度提供一种知识图谱驱动的图学习方法。

关键词: 复杂网络, 多中心生态治理, 知识图谱, 冲突测度

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

中图分类号:

许家铭, 陈光耀, 徐照. 基于知识图谱及R-GAT模型的黄河生态治理复杂网络冲突测度[J]. 图学学报, 2026, 47(5): 1125-1134.

XU Jiaming, CHEN Guangyao, XU Zhao. Conflict measurement in complex networks of Yellow River ecological governance based on knowledge graph and R-GAT model[J]. Journal of Graphics, 2026, 47(5): 1125-1134.

图/表 13

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典型治理情景	治理主体诉求			冲突链路
典型治理情景	政府	企业	社会	冲突链路
用水管理	考核与约束	成本与达标	公平与成效	考核要求提高→企业执行受压→节水效果受到质疑
生态修复	红线管控	修复投入	修复效果	修复任务推进→空间管控收紧→修复责任与效果争议显现
污染治理	制度监管	工程实施	信息公开	制度要求强化→工程治理推进→信息披露与社会监督跟进
防灾预警	平台运行	配套保障	获取信息	预警信息发布→响应措施落实→公众感知与反馈存在落差

典型治理情景	治理主体诉求			冲突链路
典型治理情景	政府	企业	社会	冲突链路
用水管理	考核与约束	成本与达标	公平与成效	考核要求提高→企业执行受压→节水效果受到质疑
生态修复	红线管控	修复投入	修复效果	修复任务推进→空间管控收紧→修复责任与效果争议显现
污染治理	制度监管	工程实施	信息公开	制度要求强化→工程治理推进→信息披露与社会监督跟进
防灾预警	平台运行	配套保障	获取信息	预警信息发布→响应措施落实→公众感知与反馈存在落差

语义标签	定义	实例	实例量
WRS	水资源统筹与节约利用	用水定额考核、节水效益评价	72
ECR	生态修复与国土保护	山水林田湖草沙一体化治理、生态保护红线监管	81
PFC	污染防治与清洁生产	VOCs治理工程、环境信息披露诉求	76
DRI	灾害防控与基础设施韧性	城市排涝系统建设、应急响应机制	71
GMC	协同治理机制	生态补偿政策、企业在线排放申报平台	102

语义标签	定义	实例	实例量
WRS	水资源统筹与节约利用	用水定额考核、节水效益评价	72
ECR	生态修复与国土保护	山水林田湖草沙一体化治理、生态保护红线监管	81
PFC	污染防治与清洁生产	VOCs治理工程、环境信息披露诉求	76
DRI	灾害防控与基础设施韧性	城市排涝系统建设、应急响应机制	71
GMC	协同治理机制	生态补偿政策、企业在线排放申报平台	102

实体标签	政府示例	社会示例	企业示例	总量
WRS	节水绩效评价	居民节水宣传活动	工业水平衡测试	21
ECR	生态保护红线监管	修复成果公示监督	绿色产业示范区	17
PFC	排污许可管理	公众举报通道	排放口在线监控	18
DRI	应急响应机制	黄河水情速报	—	7
GMC	生态补偿政策	公众听证会	在线申报平台	9