Ordering based search methods have advantages over graph based search methods for structure learning of Bayesian networks in terms on the efficiency. With the aim of further increasing the accuracy of ordering based s...Ordering based search methods have advantages over graph based search methods for structure learning of Bayesian networks in terms on the efficiency. With the aim of further increasing the accuracy of ordering based search methods, we first propose to increase the search space, which can facilitate escaping from the local optima. We present our search operators with majorizations, which are easy to implement. Experiments show that the proposed algorithm can obtain significantly more accurate results. With regard to the problem of the decrease on efficiency due to the increase of the search space, we then propose to add path priors as constraints into the swap process. We analyze the coefficient which may influence the performance of the proposed algorithm, the experiments show that the constraints can enhance the efficiency greatly, while has little effect on the accuracy. The final experiments show that, compared to other competitive methods, the proposed algorithm can find better solutions while holding high efficiency at the same time on both synthetic and real data sets.展开更多
在企业的招商引资过程中,存在多维度的风险。传统的风险评估方法由于信息失真以及经济行为中的复杂关系,难以及时且准确地识别这些风险。为解决上述问题,提出一种将大型语言模型(LLM)与图神经网络(GNN)融合的风险分析框架。利用LLM的语...在企业的招商引资过程中,存在多维度的风险。传统的风险评估方法由于信息失真以及经济行为中的复杂关系,难以及时且准确地识别这些风险。为解决上述问题,提出一种将大型语言模型(LLM)与图神经网络(GNN)融合的风险分析框架。利用LLM的语义理解能力,辅助GNN构建全面、准确的动态企业异构知识图谱,从而解决静态数据引起的信息失真问题。在此基础上,针对GNN在深度和语义表达能力上的不足,设计一个基于知识的语义结构挖掘模块,并结合Qwen2大模型增强节点表示的语义精准性。此外,提出一体化图(IOG)模块将节点分类与图分类任务统一为对“关注节点”的预测。通过统一预测机制,实现对不同图结构类型的预测,从而显著提升模型在不同数据集上的泛化能力。基于该框架构建的IOG-CIQAN(In One Graph with Collective Intelligence and Qwen2 Assistance Network)模型在劳工、财务、行政这3个风险分析数据集上的准确率均超过了87%,优于胶囊网络(CapsNet)等多种基线模型。展开更多
基金supported by the National Natural Science Fundation of China(61573285)the Doctoral Fundation of China(2013ZC53037)
文摘Ordering based search methods have advantages over graph based search methods for structure learning of Bayesian networks in terms on the efficiency. With the aim of further increasing the accuracy of ordering based search methods, we first propose to increase the search space, which can facilitate escaping from the local optima. We present our search operators with majorizations, which are easy to implement. Experiments show that the proposed algorithm can obtain significantly more accurate results. With regard to the problem of the decrease on efficiency due to the increase of the search space, we then propose to add path priors as constraints into the swap process. We analyze the coefficient which may influence the performance of the proposed algorithm, the experiments show that the constraints can enhance the efficiency greatly, while has little effect on the accuracy. The final experiments show that, compared to other competitive methods, the proposed algorithm can find better solutions while holding high efficiency at the same time on both synthetic and real data sets.
文摘在企业的招商引资过程中,存在多维度的风险。传统的风险评估方法由于信息失真以及经济行为中的复杂关系,难以及时且准确地识别这些风险。为解决上述问题,提出一种将大型语言模型(LLM)与图神经网络(GNN)融合的风险分析框架。利用LLM的语义理解能力,辅助GNN构建全面、准确的动态企业异构知识图谱,从而解决静态数据引起的信息失真问题。在此基础上,针对GNN在深度和语义表达能力上的不足,设计一个基于知识的语义结构挖掘模块,并结合Qwen2大模型增强节点表示的语义精准性。此外,提出一体化图(IOG)模块将节点分类与图分类任务统一为对“关注节点”的预测。通过统一预测机制,实现对不同图结构类型的预测,从而显著提升模型在不同数据集上的泛化能力。基于该框架构建的IOG-CIQAN(In One Graph with Collective Intelligence and Qwen2 Assistance Network)模型在劳工、财务、行政这3个风险分析数据集上的准确率均超过了87%,优于胶囊网络(CapsNet)等多种基线模型。
