异质图是由不同类型节点及边构成的图,可建模现实世界中各种类型对象及其关系。异质图嵌入旨在捕捉图中丰富的属性、结构和语义等信息,学习节点嵌入向量,用于节点分类、链接预测等任务,进而实现用户识别、商品推荐等应用。在异质图嵌入...异质图是由不同类型节点及边构成的图,可建模现实世界中各种类型对象及其关系。异质图嵌入旨在捕捉图中丰富的属性、结构和语义等信息,学习节点嵌入向量,用于节点分类、链接预测等任务,进而实现用户识别、商品推荐等应用。在异质图嵌入方法中,元路径通常被用来获取节点间的高阶结构和语义信息,然而现有方法忽略了元路径实例中不同类型节点或异质图中不同类型邻居节点的差异,导致信息丢失,进而影响节点嵌入质量。针对上述问题,提出基于数据增强的异质图注意力网络(Heterogeneous graph Attention Network based on Data Augmentation,HANDA),以更好地学习节点嵌入向量。首先,提出基于元路径邻居的边增强。该方法基于元路径获取节点的元路径邻居,用节点及其元路径邻居形成的语义边增强异质图。这些增强边不仅蕴含了节点间的高阶结构和语义,还缓解了异质图的稀疏性。其次,提出融入节点类型注意力的节点嵌入。该方法采用多头注意力从多个角度学习不同直接边邻居及增强边邻居的重要性并在注意力中融入节点的类型信息,进而通过消息传递、直接边邻居及增强边邻居同时获取节点的属性、高阶结构和语义信息,提升了节点嵌入质量。在真实数据集上的实验验证了HANDA模型在节点分类、链接预测任务上的效果优于基准模型。展开更多
According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are comput...According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.展开更多
近年来,社会化推荐作为推荐算法之一被广泛应用于各大平台.由于引入了用户的社交信息,社会化推荐可以较好地缓解数据稀疏问题.然而,大部分社会化推荐难以高效地从原始信息中提取用户的有效信息,导致引入社会信息的同时也会引入大量噪声...近年来,社会化推荐作为推荐算法之一被广泛应用于各大平台.由于引入了用户的社交信息,社会化推荐可以较好地缓解数据稀疏问题.然而,大部分社会化推荐难以高效地从原始信息中提取用户的有效信息,导致引入社会信息的同时也会引入大量噪声.为了解决上述问题,本文提出了SRBHL(Social Recommendation Based on Hypergraph embedding and Limited attention)模型,通过超图嵌入模块提取用户的历史行为信息和社交信息,以缓解原始目标用户数据稀疏问题,并结合有限注意力模块来过滤原始信息的噪声,最后将得到的有效好友信息用于推荐.在Yelp-Urbana、Yelp-Phoenix和Epinions3个真实数据集上的实验结果表明SRBHL模型相比其他的推荐算法表现更出色.此外,本文还对SRBHL模型进行了鲁棒性分析,并给出了模型最优参数的取值范围.展开更多
文摘异质图是由不同类型节点及边构成的图,可建模现实世界中各种类型对象及其关系。异质图嵌入旨在捕捉图中丰富的属性、结构和语义等信息,学习节点嵌入向量,用于节点分类、链接预测等任务,进而实现用户识别、商品推荐等应用。在异质图嵌入方法中,元路径通常被用来获取节点间的高阶结构和语义信息,然而现有方法忽略了元路径实例中不同类型节点或异质图中不同类型邻居节点的差异,导致信息丢失,进而影响节点嵌入质量。针对上述问题,提出基于数据增强的异质图注意力网络(Heterogeneous graph Attention Network based on Data Augmentation,HANDA),以更好地学习节点嵌入向量。首先,提出基于元路径邻居的边增强。该方法基于元路径获取节点的元路径邻居,用节点及其元路径邻居形成的语义边增强异质图。这些增强边不仅蕴含了节点间的高阶结构和语义,还缓解了异质图的稀疏性。其次,提出融入节点类型注意力的节点嵌入。该方法采用多头注意力从多个角度学习不同直接边邻居及增强边邻居的重要性并在注意力中融入节点的类型信息,进而通过消息传递、直接边邻居及增强边邻居同时获取节点的属性、高阶结构和语义信息,提升了节点嵌入质量。在真实数据集上的实验验证了HANDA模型在节点分类、链接预测任务上的效果优于基准模型。
基金Project(70671039) supported by the National Natural Science Foundation of China
文摘According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.
文摘近年来,社会化推荐作为推荐算法之一被广泛应用于各大平台.由于引入了用户的社交信息,社会化推荐可以较好地缓解数据稀疏问题.然而,大部分社会化推荐难以高效地从原始信息中提取用户的有效信息,导致引入社会信息的同时也会引入大量噪声.为了解决上述问题,本文提出了SRBHL(Social Recommendation Based on Hypergraph embedding and Limited attention)模型,通过超图嵌入模块提取用户的历史行为信息和社交信息,以缓解原始目标用户数据稀疏问题,并结合有限注意力模块来过滤原始信息的噪声,最后将得到的有效好友信息用于推荐.在Yelp-Urbana、Yelp-Phoenix和Epinions3个真实数据集上的实验结果表明SRBHL模型相比其他的推荐算法表现更出色.此外,本文还对SRBHL模型进行了鲁棒性分析,并给出了模型最优参数的取值范围.
