I believe that the use of Temporal Graph Neural Networks (T-GNNs) in the context of detecting sleep onset and wake from wrist-worn accelerometer data represents a cutting-edge approach in the field of sleep monitoring. T-GNNs leverage the temporal dependencies and relationships within accelerometer data to create a comprehensive understanding of an individual's sleep patterns. By modeling the dynamic interactions and patterns over time, T-GNNs can capture nuanced changes in movement and activity levels associated with transitions between wakefulness and sleep onset. In my opinion, this approach holds promise for improving the accuracy and reliability of sleep detection, potentially enhancing the overall performance of wearable sleep monitoring devices. I suggest that T-GNNs have the potential to provide valuable insights into sleep quality and duration, aiding in the assessment of sleep-related disorders and contributing to advancements in personalized sleep medicine.
\n1. \"DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training\"
\n2. \"N-STGAT: Spatio-Temporal Graph Neural Network Based Network Intrusion Detection for Near-Earth Remote Sensing\"
\n3. \"A Graph Neural Network Approach for Temporal Mesh Blending and Correspondence\"
\n4. \"Graph neural network-based spatio-temporal indoor environment prediction and optimal control for central air-conditioning systems\"
\n5. \"Localised Adaptive Spatial-Temporal Graph Neural Network\"
\n6. \"Orca: Scalable Temporal Graph Neural Network Training with Theoretical Guarantees\"
\n7. \"Spatio-Temporal Variable Structure Graph Neural Network for EEG Data Classification\"
\n8. \"Analysis of different temporal graph neural network configurations on dynamic graphs\"
\n9. \"Detecting False Data Injection Attacks using Spatial-temporal Graph Neural Network\"
\n10. \"Filter-enhance temporal graph neural network for continuous-time sequential recommendation\"
\nPlease note that I've marked \"PDF Link\" as \"#\" since the actual PDF links were not provided.
", "messages": [ { "id": 2426523, "postDate": "2023-09-06T17:15:09.060Z", "content": "I believe that the use of Temporal Graph Neural Networks (T-GNNs) in the context of detecting sleep onset and wake from wrist-worn accelerometer data represents a cutting-edge approach in the field of sleep monitoring. T-GNNs leverage the temporal dependencies and relationships within accelerometer data to create a comprehensive understanding of an individual's sleep patterns. By modeling the dynamic interactions and patterns over time, T-GNNs can capture nuanced changes in movement and activity levels associated with transitions between wakefulness and sleep onset. In my opinion, this approach holds promise for improving the accuracy and reliability of sleep detection, potentially enhancing the overall performance of wearable sleep monitoring devices. I suggest that T-GNNs have the potential to provide valuable insights into sleep quality and duration, aiding in the assessment of sleep-related disorders and contributing to advancements in personalized sleep medicine.
\n1. \"DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training\"
\n2. \"N-STGAT: Spatio-Temporal Graph Neural Network Based Network Intrusion Detection for Near-Earth Remote Sensing\"
\n3. \"A Graph Neural Network Approach for Temporal Mesh Blending and Correspondence\"
\n4. \"Graph neural network-based spatio-temporal indoor environment prediction and optimal control for central air-conditioning systems\"
\n5. \"Localised Adaptive Spatial-Temporal Graph Neural Network\"
\n6. \"Orca: Scalable Temporal Graph Neural Network Training with Theoretical Guarantees\"
\n7. \"Spatio-Temporal Variable Structure Graph Neural Network for EEG Data Classification\"
\n8. \"Analysis of different temporal graph neural network configurations on dynamic graphs\"
\n9. \"Detecting False Data Injection Attacks using Spatial-temporal Graph Neural Network\"
\n10. \"Filter-enhance temporal graph neural network for continuous-time sequential recommendation\"
\nPlease note that I've marked \"PDF Link\" as \"#\" since the actual PDF links were not provided.
", "rawMarkdown": "# Use of Temporal Graph Neural Networks to Detect Sleep States\nI believe that the use of Temporal Graph Neural Networks (T-GNNs) in the context of detecting sleep onset and wake from wrist-worn accelerometer data represents a cutting-edge approach in the field of sleep monitoring. T-GNNs leverage the temporal dependencies and relationships within accelerometer data to create a comprehensive understanding of an individual's sleep patterns. By modeling the dynamic interactions and patterns over time, T-GNNs can capture nuanced changes in movement and activity levels associated with transitions between wakefulness and sleep onset. In my opinion, this approach holds promise for improving the accuracy and reliability of sleep detection, potentially enhancing the overall performance of wearable sleep monitoring devices. I suggest that T-GNNs have the potential to provide valuable insights into sleep quality and duration, aiding in the assessment of sleep-related disorders and contributing to advancements in personalized sleep medicine.\n\n---\n## Here are some papers for T-GNNs:\n---\n\n## Literature Review:\n\n**1. \"DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training\"**\n\n- **Insights:** Temporal Graph Neural Networks (TGNNs) suffer from accuracy loss and overhead when scaling to multiple GPUs, and DistTGL addresses these challenges.\n- **Journal/Conference Name:** N/A\n\n---\n\n**2. \"N-STGAT: Spatio-Temporal Graph Neural Network Based Network Intrusion Detection for Near-Earth Remote Sensing\"**\n\n- **Contributions:**\n - Proposed a spatio-temporal graph attention network (N-STGAT)\n - Achieved high accuracy for network intrusion detection\n- **Insights:** The proposed N-STGAT achieved exceptional accuracy in network intrusion detection for near-Earth remote sensing systems.\n- **Journal/Conference Name:** Remote Sensing\n\n---\n\n**3. \"A Graph Neural Network Approach for Temporal Mesh Blending and Correspondence\"**\n\n- **Contributions:**\n - Proposed Red-Blue MPNN for mesh blending problem\n - Developed conditional refinement scheme for exact correspondence\n- **Insights:** The paper introduces a self-supervised deep learning framework for mesh blending, including the Red-Blue MPNN for estimating correspondence.\n- **Journal/Conference Name:** arXiv.org\n\n---\n\n**4. \"Graph neural network-based spatio-temporal indoor environment prediction and optimal control for central air-conditioning systems\"**\n\n- **Contributions:**\n - Proposed spatio-temporal data-driven methodology for optimal control\n - Developed graph neural network models for indoor environment prediction\n- **Insights:** The paper focuses on enhancing accuracy in indoor environment prediction and control using graph neural network models.\n- **Journal/Conference Name:** Building and Environment\n\n---\n\n**5. \"Localised Adaptive Spatial-Temporal Graph Neural Network\"**\n\n- **Contributions:**\n - Proposed Adaptive Graph Sparsification (AGS) algorithm\n - Localisation of ASTGNNs without decline in accuracy\n- **Insights:** The paper discusses the potential benefits of localising adaptive spatial-temporal graph neural networks (ASTGNNs) using graph sparsification while maintaining accuracy.\n- **Journal/Conference Name:** arXiv.org\n\n---\n\n**6. \"Orca: Scalable Temporal Graph Neural Network Training with Theoretical Guarantees\"**\n\n- **Contributions:**\n - Orca framework accelerates T-GNN training with caching\n - MRU cache replacement algorithm improves efficiency and reduces approximation errors\n- **Insights:** Orca achieves significant speedup and higher precision in training T-GNNs over baselines.\n\n---\n\n**7. \"Spatio-Temporal Variable Structure Graph Neural Network for EEG Data Classification\"**\n\n- **Insights:** The paper proposes a novel strategy for improving epilepsy diagnosis using EEG data through a variable-structure graph neural network.\n\n---\n\n**8. \"Analysis of different temporal graph neural network configurations on dynamic graphs\"**\n\n- **Contributions:**\n - Qualitative analysis of spatial-temporal dependence structure learning\n - Comparative study of the effectiveness of selected TGNs\n- **Insights:** The paper presents a comparative study of different temporal graph neural network configurations for prediction tasks on dynamic graphs.\n- **Journal/Conference Name:** arXiv.org\n\n---\n\n**9. \"Detecting False Data Injection Attacks using Spatial-temporal Graph Neural Network\"**\n\n- **Insights:** The paper discusses a method for detecting false data injection attacks using spatial-temporal graph neural networks.\n\n---\n\n**10. \"Filter-enhance temporal graph neural network for continuous-time sequential recommendation\"**\n- **Insights:** The paper introduces a filter-enhanced temporal graph neural network for continuous-time sequential recommendation.\n\nPlease note that I've marked \"PDF Link\" as \"#\" since the actual PDF links were not provided. ", "votes": 12 }, { "id": 2431944, "postDate": "2023-09-10T14:02:17.543Z", "content": "Exceptional approach indeed, but personally I will try using Transformer-based model first, since essentially transformers are graph neural networks.
", "rawMarkdown": "Exceptional approach indeed, but personally I will try using Transformer-based model first, since essentially [transformers are graph neural networks](https://thegradient.pub/transformers-are-graph-neural-networks/).", "votes": 3 }, { "id": 2505222, "postDate": "2023-10-30T13:13:20.870Z", "content": "Quite interesting approach. Maybe I'll try it, thanks for telling us about it!
", "rawMarkdown": "Quite interesting approach. Maybe I'll try it, thanks for telling us about it!" }, { "id": 2427136, "postDate": "2023-09-07T05:17:29.157Z", "content": "Can you share some code ?
", "rawMarkdown": "Can you share some code ?", "replies": [ { "id": 2428557, "postDate": "2023-09-08T02:25:19.767Z", "rawMarkdown": "", "isDeleted": true } ] } ], "comments": [ { "id": 2431944, "author_name": "Rimba Erlangga", "author_url": "", "post_date": "2023-09-10T14:02:17.543000", "content": "Exceptional approach indeed, but personally I will try using Transformer-based model first, since essentially transformers are graph neural networks.
", "votes": 3, "replies": [] }, { "id": 2505222, "author_name": "Araik Tamazian", "author_url": "", "post_date": "2023-10-30T13:13:20.870000", "content": "Quite interesting approach. Maybe I'll try it, thanks for telling us about it!
", "votes": 0, "replies": [] }, { "id": 2427136, "author_name": "Lucas Morin", "author_url": "", "post_date": "2023-09-07T05:17:29.157000", "content": "Can you share some code ?
", "votes": 0, "replies": [ { "id": 2428557, "author_name": "", "author_url": "", "post_date": "2023-09-08T02:25:19.767000", "content": "", "votes": 0, "replies": [] } ] } ], "raw_markdown_by_id": { "2426523": "# Use of Temporal Graph Neural Networks to Detect Sleep States\nI believe that the use of Temporal Graph Neural Networks (T-GNNs) in the context of detecting sleep onset and wake from wrist-worn accelerometer data represents a cutting-edge approach in the field of sleep monitoring. T-GNNs leverage the temporal dependencies and relationships within accelerometer data to create a comprehensive understanding of an individual's sleep patterns. By modeling the dynamic interactions and patterns over time, T-GNNs can capture nuanced changes in movement and activity levels associated with transitions between wakefulness and sleep onset. In my opinion, this approach holds promise for improving the accuracy and reliability of sleep detection, potentially enhancing the overall performance of wearable sleep monitoring devices. I suggest that T-GNNs have the potential to provide valuable insights into sleep quality and duration, aiding in the assessment of sleep-related disorders and contributing to advancements in personalized sleep medicine.\n\n---\n## Here are some papers for T-GNNs:\n---\n\n## Literature Review:\n\n**1. \"DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training\"**\n\n- **Insights:** Temporal Graph Neural Networks (TGNNs) suffer from accuracy loss and overhead when scaling to multiple GPUs, and DistTGL addresses these challenges.\n- **Journal/Conference Name:** N/A\n\n---\n\n**2. \"N-STGAT: Spatio-Temporal Graph Neural Network Based Network Intrusion Detection for Near-Earth Remote Sensing\"**\n\n- **Contributions:**\n - Proposed a spatio-temporal graph attention network (N-STGAT)\n - Achieved high accuracy for network intrusion detection\n- **Insights:** The proposed N-STGAT achieved exceptional accuracy in network intrusion detection for near-Earth remote sensing systems.\n- **Journal/Conference Name:** Remote Sensing\n\n---\n\n**3. \"A Graph Neural Network Approach for Temporal Mesh Blending and Correspondence\"**\n\n- **Contributions:**\n - Proposed Red-Blue MPNN for mesh blending problem\n - Developed conditional refinement scheme for exact correspondence\n- **Insights:** The paper introduces a self-supervised deep learning framework for mesh blending, including the Red-Blue MPNN for estimating correspondence.\n- **Journal/Conference Name:** arXiv.org\n\n---\n\n**4. \"Graph neural network-based spatio-temporal indoor environment prediction and optimal control for central air-conditioning systems\"**\n\n- **Contributions:**\n - Proposed spatio-temporal data-driven methodology for optimal control\n - Developed graph neural network models for indoor environment prediction\n- **Insights:** The paper focuses on enhancing accuracy in indoor environment prediction and control using graph neural network models.\n- **Journal/Conference Name:** Building and Environment\n\n---\n\n**5. \"Localised Adaptive Spatial-Temporal Graph Neural Network\"**\n\n- **Contributions:**\n - Proposed Adaptive Graph Sparsification (AGS) algorithm\n - Localisation of ASTGNNs without decline in accuracy\n- **Insights:** The paper discusses the potential benefits of localising adaptive spatial-temporal graph neural networks (ASTGNNs) using graph sparsification while maintaining accuracy.\n- **Journal/Conference Name:** arXiv.org\n\n---\n\n**6. \"Orca: Scalable Temporal Graph Neural Network Training with Theoretical Guarantees\"**\n\n- **Contributions:**\n - Orca framework accelerates T-GNN training with caching\n - MRU cache replacement algorithm improves efficiency and reduces approximation errors\n- **Insights:** Orca achieves significant speedup and higher precision in training T-GNNs over baselines.\n\n---\n\n**7. \"Spatio-Temporal Variable Structure Graph Neural Network for EEG Data Classification\"**\n\n- **Insights:** The paper proposes a novel strategy for improving epilepsy diagnosis using EEG data through a variable-structure graph neural network.\n\n---\n\n**8. \"Analysis of different temporal graph neural network configurations on dynamic graphs\"**\n\n- **Contributions:**\n - Qualitative analysis of spatial-temporal dependence structure learning\n - Comparative study of the effectiveness of selected TGNs\n- **Insights:** The paper presents a comparative study of different temporal graph neural network configurations for prediction tasks on dynamic graphs.\n- **Journal/Conference Name:** arXiv.org\n\n---\n\n**9. \"Detecting False Data Injection Attacks using Spatial-temporal Graph Neural Network\"**\n\n- **Insights:** The paper discusses a method for detecting false data injection attacks using spatial-temporal graph neural networks.\n\n---\n\n**10. \"Filter-enhance temporal graph neural network for continuous-time sequential recommendation\"**\n- **Insights:** The paper introduces a filter-enhanced temporal graph neural network for continuous-time sequential recommendation.\n\nPlease note that I've marked \"PDF Link\" as \"#\" since the actual PDF links were not provided. ", "2431944": "Exceptional approach indeed, but personally I will try using Transformer-based model first, since essentially [transformers are graph neural networks](https://thegradient.pub/transformers-are-graph-neural-networks/).", "2505222": "Quite interesting approach. Maybe I'll try it, thanks for telling us about it!", "2427136": "Can you share some code ?" } }