Due to people’s increasing dependence on social networks,it is essential to develop a consensus model considering not only their own factors but also the interaction between people.Both external trust relationship am...Due to people’s increasing dependence on social networks,it is essential to develop a consensus model considering not only their own factors but also the interaction between people.Both external trust relationship among experts and the internal reliability of experts are important factors in decision-making.This paper focuses on improving the scientificity and effectiveness of decision-making and presents a consensus model combining trust relationship among experts and expert reliability in social network group decision-making(SN-GDM).A concept named matching degree is proposed to measure expert reliability.Meanwhile,linguistic information is applied to manage the imprecise and vague information.Matching degree is expressed by a 2-tuple linguistic model,and experts’preferences are measured by a probabilistic linguistic term set(PLTS).Subsequently,a hybrid weight is explored to weigh experts’importance in a group.Then a consensus measure is introduced and a feedback mechanism is developed to produce some personalized recommendations with higher group consensus.Finally,a comparative example is provided to prove the scientificity and effectiveness of the proposed consensus model.展开更多
抽水蓄能作为电力系统中最为成熟的新能源储能技术,凭借其能调节电网负荷、平衡电力波动及提升系统稳定性的独特优势,已成为实现中国“双碳”目标的重要路径之一。因此,对抽水蓄能电站综合效益进行科学评估,是项目决策及政策制定中至关...抽水蓄能作为电力系统中最为成熟的新能源储能技术,凭借其能调节电网负荷、平衡电力波动及提升系统稳定性的独特优势,已成为实现中国“双碳”目标的重要路径之一。因此,对抽水蓄能电站综合效益进行科学评估,是项目决策及政策制定中至关重要的一环。为此,本文提出一种基于博弈论组合赋权‒云模型的综合效益评价模型。首先,运用社会网络分析法(SNA)筛选关键评价指标,构建包含财务评价、国民经济评价、技术效益、动态效益、静态效益、电网效益、综合可持续性效益和社会效益8个1级指标及其下属30个2级指标的评价指标体系。其次,采用序关系分析(G1)法和CRITIC(criteria importance through intercriteria correlation)法相结合的方式,对各评价指标进行主观与客观权重赋值。通过引入博弈论组合赋权方法,进一步优化各指标的权重分配。最终,基于云模型构建综合效益评价模型。利用博弈论组合赋权‒云模型对紫云山抽水蓄能电站进行实例分析,结果表明,该电站的综合效益评估等级为“好”,与实际情况相符,充分验证了所构建模型的有效性与准确性。该研究不仅为抽水蓄能电站的综合效益评估提供了科学的评估框架,并为类似项目的决策和实施提供了理论支持和实践依据。展开更多
社交网络群体识别在信息传播、推荐与广告等领域具有重要的研究意义与应用价值。但现有方法在特征融合、稀疏图建模及多源信息利用上仍存在不足。为此,提出一种基于结构增强与深度聚类(structure-enhanced deep clustering,SDC)的网络...社交网络群体识别在信息传播、推荐与广告等领域具有重要的研究意义与应用价值。但现有方法在特征融合、稀疏图建模及多源信息利用上仍存在不足。为此,提出一种基于结构增强与深度聚类(structure-enhanced deep clustering,SDC)的网络群体识别模型,包含4个关键模块:首先,网络拓扑增强模块通过建模节点二阶相似性生成增强邻接矩阵,缓解稀疏社交网络的高阶关系缺失;其次,多视图特征融合模块在节点级动态融合节点属性特征与拓扑特征,在图级整合原始图与增强图的语义信息;再次,多源分布融合聚类模块在分布级利用可学习权重集成不同特征空间的聚类信息,平衡局部拓扑与全局语义;最后,双重自监督模块通过KL散度(Kullback-Leibler divergence)对齐、节点重构与相似性约束进行优化。实验表明,相较于主流基线方法,SDC网络群体识别模型在3个基准数据集上的ACC、NMI、ARI、F1指标平均提升了3.80%、9.09%、11.21%和7.43%。在Facebook动态交互数据上的仿真也验证了SDC网络群体识别模型捕捉社区结构演化的能力。展开更多
基金the National Natural Science Foundation of China(71871121).
文摘Due to people’s increasing dependence on social networks,it is essential to develop a consensus model considering not only their own factors but also the interaction between people.Both external trust relationship among experts and the internal reliability of experts are important factors in decision-making.This paper focuses on improving the scientificity and effectiveness of decision-making and presents a consensus model combining trust relationship among experts and expert reliability in social network group decision-making(SN-GDM).A concept named matching degree is proposed to measure expert reliability.Meanwhile,linguistic information is applied to manage the imprecise and vague information.Matching degree is expressed by a 2-tuple linguistic model,and experts’preferences are measured by a probabilistic linguistic term set(PLTS).Subsequently,a hybrid weight is explored to weigh experts’importance in a group.Then a consensus measure is introduced and a feedback mechanism is developed to produce some personalized recommendations with higher group consensus.Finally,a comparative example is provided to prove the scientificity and effectiveness of the proposed consensus model.
文摘抽水蓄能作为电力系统中最为成熟的新能源储能技术,凭借其能调节电网负荷、平衡电力波动及提升系统稳定性的独特优势,已成为实现中国“双碳”目标的重要路径之一。因此,对抽水蓄能电站综合效益进行科学评估,是项目决策及政策制定中至关重要的一环。为此,本文提出一种基于博弈论组合赋权‒云模型的综合效益评价模型。首先,运用社会网络分析法(SNA)筛选关键评价指标,构建包含财务评价、国民经济评价、技术效益、动态效益、静态效益、电网效益、综合可持续性效益和社会效益8个1级指标及其下属30个2级指标的评价指标体系。其次,采用序关系分析(G1)法和CRITIC(criteria importance through intercriteria correlation)法相结合的方式,对各评价指标进行主观与客观权重赋值。通过引入博弈论组合赋权方法,进一步优化各指标的权重分配。最终,基于云模型构建综合效益评价模型。利用博弈论组合赋权‒云模型对紫云山抽水蓄能电站进行实例分析,结果表明,该电站的综合效益评估等级为“好”,与实际情况相符,充分验证了所构建模型的有效性与准确性。该研究不仅为抽水蓄能电站的综合效益评估提供了科学的评估框架,并为类似项目的决策和实施提供了理论支持和实践依据。
文摘社交网络群体识别在信息传播、推荐与广告等领域具有重要的研究意义与应用价值。但现有方法在特征融合、稀疏图建模及多源信息利用上仍存在不足。为此,提出一种基于结构增强与深度聚类(structure-enhanced deep clustering,SDC)的网络群体识别模型,包含4个关键模块:首先,网络拓扑增强模块通过建模节点二阶相似性生成增强邻接矩阵,缓解稀疏社交网络的高阶关系缺失;其次,多视图特征融合模块在节点级动态融合节点属性特征与拓扑特征,在图级整合原始图与增强图的语义信息;再次,多源分布融合聚类模块在分布级利用可学习权重集成不同特征空间的聚类信息,平衡局部拓扑与全局语义;最后,双重自监督模块通过KL散度(Kullback-Leibler divergence)对齐、节点重构与相似性约束进行优化。实验表明,相较于主流基线方法,SDC网络群体识别模型在3个基准数据集上的ACC、NMI、ARI、F1指标平均提升了3.80%、9.09%、11.21%和7.43%。在Facebook动态交互数据上的仿真也验证了SDC网络群体识别模型捕捉社区结构演化的能力。
