In recent years,there has been a growing interest in graph convolutional networks(GCN).However,existing GCN and variants are predominantly based on simple graph or hypergraph structures,which restricts their ability t...In recent years,there has been a growing interest in graph convolutional networks(GCN).However,existing GCN and variants are predominantly based on simple graph or hypergraph structures,which restricts their ability to handle complex data correlations in practical applications.These limitations stem from the difficulty in establishing multiple hierarchies and acquiring adaptive weights for each of them.To address this issue,this paper introduces the latest concept of complex hypergraphs and constructs a versatile high-order multi-level data correlation model.This model is realized by establishing a three-tier structure of complexes-hypergraphs-vertices.Specifically,we start by establishing hyperedge clusters on a foundational network,utilizing a second-order hypergraph structure to depict potential correlations.For this second-order structure,truncation methods are used to assess and generate a three-layer composite structure.During the construction of the composite structure,an adaptive learning strategy is implemented to merge correlations across different levels.We evaluate this model on several popular datasets and compare it with recent state-of-the-art methods.The comprehensive assessment results demonstrate that the proposed model surpasses the existing methods,particularly in modeling implicit data correlations(the classification accuracy of nodes on five public datasets Cora,Citeseer,Pubmed,Github Web ML,and Facebook are 86.1±0.33,79.2±0.35,83.1±0.46,83.8±0.23,and 80.1±0.37,respectively).This indicates that our approach possesses advantages in handling datasets with implicit multi-level structures.展开更多
To characterize the algebraic structure of wireless network coding, a hypergragh is utilized to model wireless packet networks from network layer. The algebraic description of random convolutional network coding is de...To characterize the algebraic structure of wireless network coding, a hypergragh is utilized to model wireless packet networks from network layer. The algebraic description of random convolutional network coding is deduced, and the coding condition is also presented. Analyses and simulations show that random convolutional coding is capacity-achieving with probability approaching 1.展开更多
Traffic prediction is a necessary function in intelligent transporta-tion systems to alleviate traffic congestion.Graph learning methods mainly focus on the spatiotemporal dimension,but ignore the nonlinear movement o...Traffic prediction is a necessary function in intelligent transporta-tion systems to alleviate traffic congestion.Graph learning methods mainly focus on the spatiotemporal dimension,but ignore the nonlinear movement of traffic prediction and the high-order relationships among various kinds of road segments.There exist two issues:1)deep integration of the spatiotempo-ral information and 2)global spatial dependencies for structural properties.To address these issues,we propose a nonlinear spatiotemporal optimization method,which introduces hypergraph convolution networks(HGCN).The method utilizes the higher-order spatial features of the road network captured by HGCN,and dynamically integrates them with the historical data to weigh the influence of spatiotemporal dependencies.On this basis,an extended Kalman filter is used to improve the accuracy of traffic prediction.In this study,a set of experiments were conducted on the real-world dataset in Chengdu,China.The result showed that the proposed method is feasible and accurate by two different time steps.Especially at the 15-minute time step,compared with the second-best method,the proposed method achieved 3.0%,11.7%,and 9.0%improvements in RMSE,MAE,and MAPE,respectively.展开更多
知识超图(knowledge hypergraph,KHG)是一种超图结构的知识图谱。知识超图链接预测是基于已知的实体和关系来预测缺失的实体或关系,具有重要的意义和价值。然而,现有基于神经网络的知识超图链接预测方法,只关注关系事实局部的语义特征,...知识超图(knowledge hypergraph,KHG)是一种超图结构的知识图谱。知识超图链接预测是基于已知的实体和关系来预测缺失的实体或关系,具有重要的意义和价值。然而,现有基于神经网络的知识超图链接预测方法,只关注关系事实局部的语义特征,缺乏对关系事实之间关联特征的表示学习。针对以上问题,提出了一种基于图注意力网络与卷积神经网络的链接预测方法(knowledge prediction based on GAT and convolutional neural network,HPGC)。一方面,采用改进的卷积网络(convolutional neural network,CNN)提取知识超图中节点实体表示的局部特征;另一方面,使用改进的GAT对节点和关系进行注意力建模,捕获节点之间的全局特征关系,并将两者进行融合,从而获取关系事实更全面的邻域结构,丰富超图关系事实的语义表示。此外,针对HPGC的GAT层输出矢量问题,引入多层感知机(multilayer perceptron,MLP)和正则化技术,提高模型训练的泛化能力。真实数据集上的大量实验结果验证了所提出方法的预测性能均优于基线方法。展开更多
semantics information while maintaining spatial detail con-texts.Long-range context information plays a crucial role in this scenario.How-ever,the traditional convolution kernel only provides the local and small size ...semantics information while maintaining spatial detail con-texts.Long-range context information plays a crucial role in this scenario.How-ever,the traditional convolution kernel only provides the local and small size of the receptivefield.To address the problem,we propose a plug-and-play module aggregating both local and global information(aka LGIA module)to capture the high-order relationship between nodes that are far apart.We incorporate both local and global correlations into hypergraph which is able to capture high-order rela-tionships between nodes via the concept of a hyperedge connecting a subset of nodes.The local correlation considers neighborhood nodes that are spatially adja-cent and similar in the same CNN feature maps of magnetic resonance(MR)image;and the global correlation is searched from a batch of CNN feature maps of MR images in feature space.The influence of these two correlations on seman-tic segmentation is complementary.We validated our LGIA module on various CNN segmentation models with the cardiac MR images dataset.Experimental results demonstrate that our approach outperformed several baseline models.展开更多
在线学习群体检测是在新一轮科技革命赋能教育创新变革背景下,依据学习者个性化特征优化教育资源分层配置的关键途径。现有学习趣缘社群在线学习群体的检测主要依赖学习者的直接行为记录和互动指标,较少关注学习者潜在的社交参与水平和...在线学习群体检测是在新一轮科技革命赋能教育创新变革背景下,依据学习者个性化特征优化教育资源分层配置的关键途径。现有学习趣缘社群在线学习群体的检测主要依赖学习者的直接行为记录和互动指标,较少关注学习者潜在的社交参与水平和社群结构。为营造数智环境下学习者画像决策辅助全民自主学习的文化氛围,本文提出一种社交参与视角下超图增强的学习趣缘社群群体检测方法。首先,从影响用户社交参与的维度出发,构建能够体现学习者社交参与水平的特征集。其次,提出超图卷积网络(hypergraph convolutional network,HyperGCN)增强的图聚类算法HG-SDCN(structural deep clustering network based on HyperGCN),解决了利用二分图检测在线学习群体时无法有效捕捉学习者多元交互关系和高阶结构的问题。最后,从真实学习趣缘社群收集数据,验证本文提出方法的检测效果。与基线相比,本文方法在Acc(accuracy)、F1、NMI(normalized mutual information)和ARI(adjusted Rand index)等评价指标上分别提升了16.16、9.77、16.01和22.14个百分点。上述结果不仅证明了HyperGCN在捕捉学习者高阶结构实现在线学习群体检测任务中的有效性,还为未来从社交参与维度制定调整个性化教育资源配置策略提供了方法和理论支撑。展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12275179 and 11875042)the Natural Science Foundation of Shanghai Municipality,China(Grant No.21ZR1443900)。
文摘In recent years,there has been a growing interest in graph convolutional networks(GCN).However,existing GCN and variants are predominantly based on simple graph or hypergraph structures,which restricts their ability to handle complex data correlations in practical applications.These limitations stem from the difficulty in establishing multiple hierarchies and acquiring adaptive weights for each of them.To address this issue,this paper introduces the latest concept of complex hypergraphs and constructs a versatile high-order multi-level data correlation model.This model is realized by establishing a three-tier structure of complexes-hypergraphs-vertices.Specifically,we start by establishing hyperedge clusters on a foundational network,utilizing a second-order hypergraph structure to depict potential correlations.For this second-order structure,truncation methods are used to assess and generate a three-layer composite structure.During the construction of the composite structure,an adaptive learning strategy is implemented to merge correlations across different levels.We evaluate this model on several popular datasets and compare it with recent state-of-the-art methods.The comprehensive assessment results demonstrate that the proposed model surpasses the existing methods,particularly in modeling implicit data correlations(the classification accuracy of nodes on five public datasets Cora,Citeseer,Pubmed,Github Web ML,and Facebook are 86.1±0.33,79.2±0.35,83.1±0.46,83.8±0.23,and 80.1±0.37,respectively).This indicates that our approach possesses advantages in handling datasets with implicit multi-level structures.
基金Supported by National Natural Science Foundation of China (No.61271174)Young Teachers' Innovation Foundation of Xidian University(K5051303137)
文摘To characterize the algebraic structure of wireless network coding, a hypergragh is utilized to model wireless packet networks from network layer. The algebraic description of random convolutional network coding is deduced, and the coding condition is also presented. Analyses and simulations show that random convolutional coding is capacity-achieving with probability approaching 1.
文摘Traffic prediction is a necessary function in intelligent transporta-tion systems to alleviate traffic congestion.Graph learning methods mainly focus on the spatiotemporal dimension,but ignore the nonlinear movement of traffic prediction and the high-order relationships among various kinds of road segments.There exist two issues:1)deep integration of the spatiotempo-ral information and 2)global spatial dependencies for structural properties.To address these issues,we propose a nonlinear spatiotemporal optimization method,which introduces hypergraph convolution networks(HGCN).The method utilizes the higher-order spatial features of the road network captured by HGCN,and dynamically integrates them with the historical data to weigh the influence of spatiotemporal dependencies.On this basis,an extended Kalman filter is used to improve the accuracy of traffic prediction.In this study,a set of experiments were conducted on the real-world dataset in Chengdu,China.The result showed that the proposed method is feasible and accurate by two different time steps.Especially at the 15-minute time step,compared with the second-best method,the proposed method achieved 3.0%,11.7%,and 9.0%improvements in RMSE,MAE,and MAPE,respectively.
文摘知识超图(knowledge hypergraph,KHG)是一种超图结构的知识图谱。知识超图链接预测是基于已知的实体和关系来预测缺失的实体或关系,具有重要的意义和价值。然而,现有基于神经网络的知识超图链接预测方法,只关注关系事实局部的语义特征,缺乏对关系事实之间关联特征的表示学习。针对以上问题,提出了一种基于图注意力网络与卷积神经网络的链接预测方法(knowledge prediction based on GAT and convolutional neural network,HPGC)。一方面,采用改进的卷积网络(convolutional neural network,CNN)提取知识超图中节点实体表示的局部特征;另一方面,使用改进的GAT对节点和关系进行注意力建模,捕获节点之间的全局特征关系,并将两者进行融合,从而获取关系事实更全面的邻域结构,丰富超图关系事实的语义表示。此外,针对HPGC的GAT层输出矢量问题,引入多层感知机(multilayer perceptron,MLP)和正则化技术,提高模型训练的泛化能力。真实数据集上的大量实验结果验证了所提出方法的预测性能均优于基线方法。
基金supported by the Sichuan Science and Technology Program(Grant No.2019ZDZX0005,2019YFG0496,2020YFG0143,2019JDJQ0002 and 2020YFG0009).
文摘semantics information while maintaining spatial detail con-texts.Long-range context information plays a crucial role in this scenario.How-ever,the traditional convolution kernel only provides the local and small size of the receptivefield.To address the problem,we propose a plug-and-play module aggregating both local and global information(aka LGIA module)to capture the high-order relationship between nodes that are far apart.We incorporate both local and global correlations into hypergraph which is able to capture high-order rela-tionships between nodes via the concept of a hyperedge connecting a subset of nodes.The local correlation considers neighborhood nodes that are spatially adja-cent and similar in the same CNN feature maps of magnetic resonance(MR)image;and the global correlation is searched from a batch of CNN feature maps of MR images in feature space.The influence of these two correlations on seman-tic segmentation is complementary.We validated our LGIA module on various CNN segmentation models with the cardiac MR images dataset.Experimental results demonstrate that our approach outperformed several baseline models.
文摘在线学习群体检测是在新一轮科技革命赋能教育创新变革背景下,依据学习者个性化特征优化教育资源分层配置的关键途径。现有学习趣缘社群在线学习群体的检测主要依赖学习者的直接行为记录和互动指标,较少关注学习者潜在的社交参与水平和社群结构。为营造数智环境下学习者画像决策辅助全民自主学习的文化氛围,本文提出一种社交参与视角下超图增强的学习趣缘社群群体检测方法。首先,从影响用户社交参与的维度出发,构建能够体现学习者社交参与水平的特征集。其次,提出超图卷积网络(hypergraph convolutional network,HyperGCN)增强的图聚类算法HG-SDCN(structural deep clustering network based on HyperGCN),解决了利用二分图检测在线学习群体时无法有效捕捉学习者多元交互关系和高阶结构的问题。最后,从真实学习趣缘社群收集数据,验证本文提出方法的检测效果。与基线相比,本文方法在Acc(accuracy)、F1、NMI(normalized mutual information)和ARI(adjusted Rand index)等评价指标上分别提升了16.16、9.77、16.01和22.14个百分点。上述结果不仅证明了HyperGCN在捕捉学习者高阶结构实现在线学习群体检测任务中的有效性,还为未来从社交参与维度制定调整个性化教育资源配置策略提供了方法和理论支撑。
