Accurate traffic pattern prediction in largescale networks is of great importance for intelligent system management and automatic resource allocation.System-level mobile traffic forecasting has significant challenges ...Accurate traffic pattern prediction in largescale networks is of great importance for intelligent system management and automatic resource allocation.System-level mobile traffic forecasting has significant challenges due to the tremendous temporal and spatial dynamics introduced by diverse Internet user behaviors and frequent traffic migration.Spatialtemporal graph modeling is an efficient approach for analyzing the spatial relations and temporal trends of mobile traffic in a large system.Previous research may not reflect the optimal dependency by ignoring inter-base station dependency or pre-determining the explicit geological distance as the interrelationship of base stations.To overcome the limitations of graph structure,this study proposes an adaptive graph convolutional network(AGCN)that captures the latent spatial dependency by developing self-adaptive dependency matrices and acquires temporal dependency using recurrent neural networks.Evaluated on two mobile network datasets,the experimental results demonstrate that this method outperforms other baselines and reduces the mean absolute error by 3.7%and 5.6%compared to time-series based approaches.展开更多
Dementia is a syndrome causing a progressive loss of brain function and mainly includes subtypes,such as Alzheimer’s Disease(AD),FrontoTemporal Dementia(FTD),and Vascular Cognitive Impairment(VCI).Electroencephalogra...Dementia is a syndrome causing a progressive loss of brain function and mainly includes subtypes,such as Alzheimer’s Disease(AD),FrontoTemporal Dementia(FTD),and Vascular Cognitive Impairment(VCI).Electroencephalography(EEG)is widely used in dementia diagnosis to detect brain electrophysiological signals efficiently.However,the small number of samples available in EEG-based dementia diagnosis results in poor performance of existing methods.To address this issue,we propose a Multi-scale Adaptive Graph Learning based on Multi-wave EEG data(MAGLM)for dementia diagnosis.Firstly,we extract both time-domain and frequency-domain features of multi-wave EEG data.Secondly,to reliably expand the insufficient samples,we propose a multi-wave EEG data augmentation model based on generative learning.Finally,to explore the rich patterns between scales,waves,and samples,we propose a multi-scale adaptive graph learning model to perform dementia diagnosis based on augmented EEG data.MAGLM is validated on an in-house EEG dataset,including AD,FTD,and VCI.The experimental and visualization results show the superiority of the proposed MAGLM over the state-of-the-art methods.In conclusion,MAGLM is not only effective in dementia diagnosis,but also provides experience for EEG-based brain science research.展开更多
In recent years,aquaculture has developed rapidly,especially in coastal and open ocean areas.In practice,water quality prediction is of critical importance.However,traditional water quality prediction models face limi...In recent years,aquaculture has developed rapidly,especially in coastal and open ocean areas.In practice,water quality prediction is of critical importance.However,traditional water quality prediction models face limitations in handling complex spatiotemporal patterns.To address this challenge,a prediction model was proposed for water quality,namely an adaptive multi-channel temporal graph convolutional network(AMTGCN).The AMTGCN integrates adaptive graph construction,multi-channel spatiotemporal graph convolutional network,and fusion layers,and can comprehensively capture the spatial relationships and spatiotemporal patterns in aquaculture water quality data.Onsite aquaculture water quality data and the metrics MAE,RMSE,MAPE,and R^(2) were collected to validate the AMTGCN.The results show that the AMTGCN presents an average improvement of 34.01%,34.59%,36.05%,and 17.71%compared to LSTM,respectively;an average improvement of 64.84%,56.78%,64.82%,and 153.16%compared to the STGCN,respectively;an average improvement of 55.25%,48.67%,57.01%,and 209.00%compared to GCN-LSTM,respectively;and an average improvement of 7.05%,5.66%,7.42%,and 2.47%compared to TCN,respectively.This indicates that the AMTGCN,integrating the innovative structure of adaptive graph construction and multi-channel spatiotemporal graph convolutional network,could provide an efficient solution for water quality prediction in aquaculture.展开更多
Graph representation learning often faces knowledge scarcity in real-world applications,including limited labels and sparse relationships.Although a range of methods have been proposed to address these problems,such a...Graph representation learning often faces knowledge scarcity in real-world applications,including limited labels and sparse relationships.Although a range of methods have been proposed to address these problems,such as graph few-shot learning,they mainly rely on inadequate knowledge within the task graph,which would limit their effectiveness.Moreover,they fail to consider other potentially useful task-related graphs.To overcome these limitations,domain adaptation for graph representation learning has emerged as an effective paradigm for transferring knowledge across graphs.It is also recognized as graph domain adaptation(GDA).In particular,to enhance model performance on target graphs with specific tasks,GDA introduces a bunch of task-related graphs as source graphs and adapts the knowledge learnt from source graphs to the target graphs.Since GDA combines the advantages of graph representation learning and domain adaptation,it has become a promising direction of transfer learning on graphs and has attracted an increasing amount of research interest in recent years.In this paper,we comprehensively overview the studies of GDA and present a detailed survey of recent advances.Specifically,we outline the current research status,analyze key challenges,propose a taxonomy,introduce representative work and practical applications,and discuss future prospects.To the best of our knowledge,this paper is the first survey for graph domain adaptation.展开更多
基金supported by the National Natural Science Foundation of China(61975020,62171053)。
文摘Accurate traffic pattern prediction in largescale networks is of great importance for intelligent system management and automatic resource allocation.System-level mobile traffic forecasting has significant challenges due to the tremendous temporal and spatial dynamics introduced by diverse Internet user behaviors and frequent traffic migration.Spatialtemporal graph modeling is an efficient approach for analyzing the spatial relations and temporal trends of mobile traffic in a large system.Previous research may not reflect the optimal dependency by ignoring inter-base station dependency or pre-determining the explicit geological distance as the interrelationship of base stations.To overcome the limitations of graph structure,this study proposes an adaptive graph convolutional network(AGCN)that captures the latent spatial dependency by developing self-adaptive dependency matrices and acquires temporal dependency using recurrent neural networks.Evaluated on two mobile network datasets,the experimental results demonstrate that this method outperforms other baselines and reduces the mean absolute error by 3.7%and 5.6%compared to time-series based approaches.
基金supported by the National Key R&D Program of China(No.2023YFC3603700)the National Natural Science Foundation of China(No.62172444)+3 种基金the Shenzhen Science and Technology Program(No.KQTD20200820113106007)the Xinjiang Key Research and Development project(No.2023B01032)the Central South University Innovation-Driven Research Programme(No.2023CXQD018)the High Performance Computing Center of Central South University,China.
文摘Dementia is a syndrome causing a progressive loss of brain function and mainly includes subtypes,such as Alzheimer’s Disease(AD),FrontoTemporal Dementia(FTD),and Vascular Cognitive Impairment(VCI).Electroencephalography(EEG)is widely used in dementia diagnosis to detect brain electrophysiological signals efficiently.However,the small number of samples available in EEG-based dementia diagnosis results in poor performance of existing methods.To address this issue,we propose a Multi-scale Adaptive Graph Learning based on Multi-wave EEG data(MAGLM)for dementia diagnosis.Firstly,we extract both time-domain and frequency-domain features of multi-wave EEG data.Secondly,to reliably expand the insufficient samples,we propose a multi-wave EEG data augmentation model based on generative learning.Finally,to explore the rich patterns between scales,waves,and samples,we propose a multi-scale adaptive graph learning model to perform dementia diagnosis based on augmented EEG data.MAGLM is validated on an in-house EEG dataset,including AD,FTD,and VCI.The experimental and visualization results show the superiority of the proposed MAGLM over the state-of-the-art methods.In conclusion,MAGLM is not only effective in dementia diagnosis,but also provides experience for EEG-based brain science research.
基金funded by the National Key Research and Development Program of China:Sino-Malta Fund 2022“Autonomous Biomimetic Underwater Vehicle for Digital Cage Monitoring”(Grant No.2022YFE0107100).
文摘In recent years,aquaculture has developed rapidly,especially in coastal and open ocean areas.In practice,water quality prediction is of critical importance.However,traditional water quality prediction models face limitations in handling complex spatiotemporal patterns.To address this challenge,a prediction model was proposed for water quality,namely an adaptive multi-channel temporal graph convolutional network(AMTGCN).The AMTGCN integrates adaptive graph construction,multi-channel spatiotemporal graph convolutional network,and fusion layers,and can comprehensively capture the spatial relationships and spatiotemporal patterns in aquaculture water quality data.Onsite aquaculture water quality data and the metrics MAE,RMSE,MAPE,and R^(2) were collected to validate the AMTGCN.The results show that the AMTGCN presents an average improvement of 34.01%,34.59%,36.05%,and 17.71%compared to LSTM,respectively;an average improvement of 64.84%,56.78%,64.82%,and 153.16%compared to the STGCN,respectively;an average improvement of 55.25%,48.67%,57.01%,and 209.00%compared to GCN-LSTM,respectively;and an average improvement of 7.05%,5.66%,7.42%,and 2.47%compared to TCN,respectively.This indicates that the AMTGCN,integrating the innovative structure of adaptive graph construction and multi-channel spatiotemporal graph convolutional network,could provide an efficient solution for water quality prediction in aquaculture.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(CAS)under Grant No.XDB0680302the National Key Research and Development Program of China under Grant No.2023YFC3305303+2 种基金the National Natural Science Foundation of China under Grant Nos.62372434 and 62302485the China Postdoctoral Science Foundation under Grant No.2022M713206the CAS Special Research Assistant Program,and the Key Research Project of Chinese Academy of Sciences under Grant No.RCJJ-145-24-21.
文摘Graph representation learning often faces knowledge scarcity in real-world applications,including limited labels and sparse relationships.Although a range of methods have been proposed to address these problems,such as graph few-shot learning,they mainly rely on inadequate knowledge within the task graph,which would limit their effectiveness.Moreover,they fail to consider other potentially useful task-related graphs.To overcome these limitations,domain adaptation for graph representation learning has emerged as an effective paradigm for transferring knowledge across graphs.It is also recognized as graph domain adaptation(GDA).In particular,to enhance model performance on target graphs with specific tasks,GDA introduces a bunch of task-related graphs as source graphs and adapts the knowledge learnt from source graphs to the target graphs.Since GDA combines the advantages of graph representation learning and domain adaptation,it has become a promising direction of transfer learning on graphs and has attracted an increasing amount of research interest in recent years.In this paper,we comprehensively overview the studies of GDA and present a detailed survey of recent advances.Specifically,we outline the current research status,analyze key challenges,propose a taxonomy,introduce representative work and practical applications,and discuss future prospects.To the best of our knowledge,this paper is the first survey for graph domain adaptation.
