动态网络链路预测广泛的应用前景,使得其逐渐成为网络科学研究的热点.动态网络链路演化过程中具有复杂的空间相关性和时间依赖性,导致其链路预测任务极具挑战.提出一个基于时序图卷积的动态网络链路预测模型(dynamic network link predi...动态网络链路预测广泛的应用前景,使得其逐渐成为网络科学研究的热点.动态网络链路演化过程中具有复杂的空间相关性和时间依赖性,导致其链路预测任务极具挑战.提出一个基于时序图卷积的动态网络链路预测模型(dynamic network link prediction based on sequential graph convolution, DNLP-SGC).针对网络快照序列不能有效反映动态网络连续性的问题,采用边缘触发机制对原始网络权重矩阵进行修正,弥补了离散快照表示动态网络存在时序信息丢失的不足.从网络演化过程出发,综合考虑节点间的特征相似性以及历史交互信息,采用时序图卷积提取动态网络中节点的特征,该方法融合了节点时空依赖关系.进一步,采用因果卷积网络捕获网络演化过程中潜在的全局时序特征,实现动态网络链路预测.在2个真实的网络数据集上的实验结果表明,DNLP-SGC在precision, recall, AUC指标上均优于对比的基线模型.展开更多
为了及早发现重症监护室中的急性肾损伤高危患者,为其提供适当的护理,实现医疗资源的合理利用,研究建立因果贝叶斯网络模型进行急性肾损伤高危患者死亡风险预测。从重症监护医学信息市场(Medical Information Mart for Intensive CareⅢ...为了及早发现重症监护室中的急性肾损伤高危患者,为其提供适当的护理,实现医疗资源的合理利用,研究建立因果贝叶斯网络模型进行急性肾损伤高危患者死亡风险预测。从重症监护医学信息市场(Medical Information Mart for Intensive CareⅢ,MIMIC-Ⅲ)数据库中筛选了25个研究变量和3870条患者数据,使用因果发现算法进行特征降维。通过NO TEARS算法构建因果图并建立因果贝叶斯网络进行实验,通过机器学习算法验证重要特征的合理性,并对网络结构进行因果效应估计,模型具有最高的受试者工作特征曲线下面积(Area Under the Receiver Operating Characteristic,AUROC)分数,为81.7%,优于逻辑回归(Logistic Regression,LR)、随机森林(Random Forest,RF)和极端梯度提升树(eXtreme Gradient Boosting,XGBoost)。此外,模型的重要特征预测能力在各种建模中都很稳健,构建的因果贝叶斯网络具有更好的预测效果并具备良好的解释能力。展开更多
Causal analysis is a powerful tool to unravel the data complexity and hence provide clues to achieving, say, better platform design, efficient interoperability and service management, etc. Data science will surely ben...Causal analysis is a powerful tool to unravel the data complexity and hence provide clues to achieving, say, better platform design, efficient interoperability and service management, etc. Data science will surely benefit from the advancement in this field. Here we introduce into this community a recent finding in physics on causality and the subsequent rigorous and quantitative causality analysis. The resulting formula is concise in form, involving only the common statistics namely sample covariance. A corollary is that causation implies correlation, but not vice versa, resolving the long-standing philosophical debate over correlation versus causation. The applicability to big data analysis is validated with time series purportedly generated with hidden processes. As a demonstration, a preliminary application to the gross domestic product (GDP) data of United States, China, and Japan reveals some subtle USA-China-Japan relations in certain periods. 展开更多
文摘动态网络链路预测广泛的应用前景,使得其逐渐成为网络科学研究的热点.动态网络链路演化过程中具有复杂的空间相关性和时间依赖性,导致其链路预测任务极具挑战.提出一个基于时序图卷积的动态网络链路预测模型(dynamic network link prediction based on sequential graph convolution, DNLP-SGC).针对网络快照序列不能有效反映动态网络连续性的问题,采用边缘触发机制对原始网络权重矩阵进行修正,弥补了离散快照表示动态网络存在时序信息丢失的不足.从网络演化过程出发,综合考虑节点间的特征相似性以及历史交互信息,采用时序图卷积提取动态网络中节点的特征,该方法融合了节点时空依赖关系.进一步,采用因果卷积网络捕获网络演化过程中潜在的全局时序特征,实现动态网络链路预测.在2个真实的网络数据集上的实验结果表明,DNLP-SGC在precision, recall, AUC指标上均优于对比的基线模型.
文摘为了及早发现重症监护室中的急性肾损伤高危患者,为其提供适当的护理,实现医疗资源的合理利用,研究建立因果贝叶斯网络模型进行急性肾损伤高危患者死亡风险预测。从重症监护医学信息市场(Medical Information Mart for Intensive CareⅢ,MIMIC-Ⅲ)数据库中筛选了25个研究变量和3870条患者数据,使用因果发现算法进行特征降维。通过NO TEARS算法构建因果图并建立因果贝叶斯网络进行实验,通过机器学习算法验证重要特征的合理性,并对网络结构进行因果效应估计,模型具有最高的受试者工作特征曲线下面积(Area Under the Receiver Operating Characteristic,AUROC)分数,为81.7%,优于逻辑回归(Logistic Regression,LR)、随机森林(Random Forest,RF)和极端梯度提升树(eXtreme Gradient Boosting,XGBoost)。此外,模型的重要特征预测能力在各种建模中都很稳健,构建的因果贝叶斯网络具有更好的预测效果并具备良好的解释能力。
文摘Causal analysis is a powerful tool to unravel the data complexity and hence provide clues to achieving, say, better platform design, efficient interoperability and service management, etc. Data science will surely benefit from the advancement in this field. Here we introduce into this community a recent finding in physics on causality and the subsequent rigorous and quantitative causality analysis. The resulting formula is concise in form, involving only the common statistics namely sample covariance. A corollary is that causation implies correlation, but not vice versa, resolving the long-standing philosophical debate over correlation versus causation. The applicability to big data analysis is validated with time series purportedly generated with hidden processes. As a demonstration, a preliminary application to the gross domestic product (GDP) data of United States, China, and Japan reveals some subtle USA-China-Japan relations in certain periods.
