The existing graph convolution methods usually suffer high computational burdens,large memory requirements,and intractable batch-processing.In this paper,we propose a high-efficient variational gridded graph convoluti...The existing graph convolution methods usually suffer high computational burdens,large memory requirements,and intractable batch-processing.In this paper,we propose a high-efficient variational gridded graph convolution network(VG-GCN)to encode non-regular graph data,which overcomes all these aforementioned problems.To capture graph topology structures efficiently,in the proposed framework,we propose a hierarchically-coarsened random walk(hcr-walk)by taking advantage of the classic random walk and node/edge encapsulation.The hcr-walk greatly mitigates the problem of exponentially explosive sampling times which occur in the classic version,while preserving graph structures well.To efficiently encode local hcr-walk around one reference node,we project hcrwalk into an ordered space to form image-like grid data,which favors those conventional convolution networks.Instead of the direct 2-D convolution filtering,a variational convolution block(VCB)is designed to model the distribution of the randomsampling hcr-walk inspired by the well-formulated variational inference.We experimentally validate the efficiency and effectiveness of our proposed VG-GCN,which has high computation speed,and the comparable or even better performance when compared with baseline GCNs.展开更多
针对图表示学习模型依赖具体任务进行特征保留以及节点表示的泛化性有限等问题,本文提出一种基于自监督信息增强的图表示学习模型(Self-Variational Graph Auto Encoder,Self-VGAE)。Self-VGAE首先使用图卷积编码器和节点表示内积解码...针对图表示学习模型依赖具体任务进行特征保留以及节点表示的泛化性有限等问题,本文提出一种基于自监督信息增强的图表示学习模型(Self-Variational Graph Auto Encoder,Self-VGAE)。Self-VGAE首先使用图卷积编码器和节点表示内积解码器构建变分图自编码器(Variational Graph Auto Encoder,VGAE),并对原始图进行特征提取和编码;然后,使用拓扑结构和节点属性生成自监督信息,在模型训练过程中约束节点表示的生成。在多个图分析任务中,Self-VGAE的实验表现均优于当前较为先进的基线模型,表明引入自监督信息能够增强对节点特征相似性和差异性的保留能力以及对拓扑结构的保持、推断能力,并且Self-VGAE具有较强的泛化能力。展开更多
Stocks that are fundamentally connected with each other tend to move together.Considering such common trends is believed to benefit stock movement forecasting tasks.However,such signals are not trivial to model becaus...Stocks that are fundamentally connected with each other tend to move together.Considering such common trends is believed to benefit stock movement forecasting tasks.However,such signals are not trivial to model because the connections among stocks are not physically presented and need to be estimated from volatile data.Motivated by this observation,we propose a framework that incorporates the inter-connection of firms to forecast stock prices.To effectively utilize a large set of fundamental features,we further design a novel pipeline.First,we use variational autoencoder(VAE)to reduce the dimension of stock fundamental information and then cluster stocks into a graph structure(fundamentally clustering).Second,a hybrid model of graph convolutional network and long-short term memory network(GCN-LSTM)with an adjacency graph matrix(learnt from VAE)is proposed for graph-structured stock market forecasting.Experiments on minute-level U.S.stock market data demonstrate that our model effectively captures both spatial and temporal signals and achieves superior improvement over baseline methods.The proposed model is promising for other applications in which there is a possible but hidden spatial dependency to improve time-series prediction.展开更多
基金supported by the Natural Science Foundation of Jiangsu Province(BK20190019,BK20190452)the National Natural Science Foundation of China(62072244,61906094)the Natural Science Foundation of Shandong Province(ZR2020LZH008)。
文摘The existing graph convolution methods usually suffer high computational burdens,large memory requirements,and intractable batch-processing.In this paper,we propose a high-efficient variational gridded graph convolution network(VG-GCN)to encode non-regular graph data,which overcomes all these aforementioned problems.To capture graph topology structures efficiently,in the proposed framework,we propose a hierarchically-coarsened random walk(hcr-walk)by taking advantage of the classic random walk and node/edge encapsulation.The hcr-walk greatly mitigates the problem of exponentially explosive sampling times which occur in the classic version,while preserving graph structures well.To efficiently encode local hcr-walk around one reference node,we project hcrwalk into an ordered space to form image-like grid data,which favors those conventional convolution networks.Instead of the direct 2-D convolution filtering,a variational convolution block(VCB)is designed to model the distribution of the randomsampling hcr-walk inspired by the well-formulated variational inference.We experimentally validate the efficiency and effectiveness of our proposed VG-GCN,which has high computation speed,and the comparable or even better performance when compared with baseline GCNs.
文摘针对图表示学习模型依赖具体任务进行特征保留以及节点表示的泛化性有限等问题,本文提出一种基于自监督信息增强的图表示学习模型(Self-Variational Graph Auto Encoder,Self-VGAE)。Self-VGAE首先使用图卷积编码器和节点表示内积解码器构建变分图自编码器(Variational Graph Auto Encoder,VGAE),并对原始图进行特征提取和编码;然后,使用拓扑结构和节点属性生成自监督信息,在模型训练过程中约束节点表示的生成。在多个图分析任务中,Self-VGAE的实验表现均优于当前较为先进的基线模型,表明引入自监督信息能够增强对节点特征相似性和差异性的保留能力以及对拓扑结构的保持、推断能力,并且Self-VGAE具有较强的泛化能力。
文摘Stocks that are fundamentally connected with each other tend to move together.Considering such common trends is believed to benefit stock movement forecasting tasks.However,such signals are not trivial to model because the connections among stocks are not physically presented and need to be estimated from volatile data.Motivated by this observation,we propose a framework that incorporates the inter-connection of firms to forecast stock prices.To effectively utilize a large set of fundamental features,we further design a novel pipeline.First,we use variational autoencoder(VAE)to reduce the dimension of stock fundamental information and then cluster stocks into a graph structure(fundamentally clustering).Second,a hybrid model of graph convolutional network and long-short term memory network(GCN-LSTM)with an adjacency graph matrix(learnt from VAE)is proposed for graph-structured stock market forecasting.Experiments on minute-level U.S.stock market data demonstrate that our model effectively captures both spatial and temporal signals and achieves superior improvement over baseline methods.The proposed model is promising for other applications in which there is a possible but hidden spatial dependency to improve time-series prediction.
