This is the repository for the collection of applying Graph Neural Networks in Internet of Things (IoT).
If you find this repository helpful, you may consider cite our work:
Guimin Dong, Mingyue Tang, Zhiyuan Wang, Jiechao Gao, Sikun Guo, Lihua Cai, Robert Gutierrez, Bradford Campbell, Laura E. Barnes, Mehdi Boukhechba, Graph Neural Networks in IoT: A Survey.
We categorize GNNs in IoT into the following groups based on their semantics of graph modeling:
1）Multi-agent Interaction, 2）Human State Dynamics, and 3）IoT Sensor Interconnection.
Multi-agent Interaction:
Zhou, Yang, et al. "Multi-Robot Collaborative Perception with Graph Neural Networks." IEEE Robotics and Automation Letters (2022). Link
Li, Zirui, et al. "Interactive Behavior Prediction for Heterogeneous Traffic Participants in the Urban Road: A Graph-Neural-Network-Based Multitask Learning Framework." IEEE/ASME Transactions on Mechatronics 26.3 (2021): 1339-1349. Link
Jo, Eunsan, Myoungho Sunwoo, and Minchul Lee. "Vehicle Trajectory Prediction Using Hierarchical Graph Neural Network for Considering Interaction among Multimodal Maneuvers." Sensors 21.16 (2021): 5354. Link
Rangesh, Akshay, et al. "Trackmpnn: A message passing graph neural architecture for multi-object tracking." arXiv preprint arXiv:2101.04206 (2021). Link
Chen, Sikai, et al. "Graph neural network and reinforcement learning for multi‐agent cooperative control of connected autonomous vehicles." Computer‐Aided Civil and Infrastructure Engineering 36.7 (2021): 838-857. Link
Zhou, Lifeng, et al. "Graph neural networks for decentralized multi-robot submodular action selection." arXiv preprint arXiv:2105.08601 (2021). Link
Ma, Hengbo, et al. "Continual multi-agent interaction behavior prediction with conditional generative memory." IEEE Robotics and Automation Letters 6.4 (2021): 8410-8417. Link
Dong, Bo, et al. "Multi-modal trajectory prediction for autonomous driving with semantic map and dynamic graph attention network." arXiv preprint arXiv:2103.16273 (2021). Link
Weng, Xinshuo, Ye Yuan, and Kris Kitani. "PTP: Parallelized tracking and prediction with graph neural networks and diversity sampling." IEEE Robotics and Automation Letters 6.3 (2021): 4640-4647. Link
Chen, Junan, et al. "Spatial-Temporal Graph Neural Network For Interaction-Aware Vehicle Trajectory Prediction." 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE). IEEE, 2021. Link
Li, Kunming, et al. "Attentional-GCNN: Adaptive Pedestrian Trajectory Prediction towards Generic Autonomous Vehicle Use Cases." 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021. Link
Casas, Sergio, et al. "Spagnn: Spatially-aware graph neural networks for relational behavior forecasting from sensor data." 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020. Link
Tolstaya, Ekaterina, et al. "Multi-robot coverage and exploration using spatial graph neural networks." 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020. Link
Mo, Xiaoyu, Yang Xing, and Chen Lv. "Recog: A deep learning framework with heterogeneous graph for interaction-aware trajectory prediction." arXiv preprint arXiv:2012.05032 (2020). Link
Mohamed, Abduallah, et al. "Social-stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020. Link
Eiffert, Stuart, et al. "Probabilistic crowd GAN: Multimodal pedestrian trajectory prediction using a graph vehicle-pedestrian attention network." IEEE Robotics and Automation Letters 5.4 (2020): 5026-5033. Link
Li, Jiachen, et al. "Social-wagdat: Interaction-aware trajectory prediction via wasserstein graph double-attention network." arXiv preprint arXiv:2002.06241 (2020). Link
Lee, Donsuk, et al. "Joint interaction and trajectory prediction for autonomous driving using graph neural networks." arXiv preprint arXiv:1912.07882 (2019). Link
Kosaraju, Vineet, et al. "Social-bigat: Multimodal trajectory forecasting using bicycle-gan and graph attention networks." Advances in Neural Information Processing Systems 32 (2019). Link
Human State Dynamics:
Dong, Guimin, et al. "Semi-supervised Graph Instance Transformer for Mental Health Inference." 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2021. Link
Li, Jingcong, et al. "Cross-Subject EEG Emotion Recognition With Self-Organized Graph Neural Network." Frontiers in Neuroscience (2021): 689. Link
Dong, Guimin, et al. "Influenza-like symptom recognition using mobile sensing and graph neural networks." Proceedings of the Conference on Health, Inference, and Learning. 2021. Link
Dong, Guimin, et al. "Using graph representation learning to predict salivary cortisol levels in pancreatic cancer patients." Journal of Healthcare Informatics Research 5.4 (2021): 401-419. Link
Wagh, Neeraj, and Yogatheesan Varatharajah. "Eeg-gcnn: Augmenting electroencephalogram-based neurological disease diagnosis using a domain-guided graph convolutional neural network." Machine Learning for Health. PMLR, 2020. Link
Lun, Xiangmin, et al. "GCNs-net: a graph convolutional neural network approach for decoding time-resolved eeg motor imagery signals." arXiv preprint arXiv:2006.08924 (2020). Link
Wagh, Neeraj, and Yogatheesan Varatharajah. "Eeg-gcnn: Augmenting electroencephalogram-based neurological disease diagnosis using a domain-guided graph convolutional neural network." Machine Learning for Health. PMLR, 2020. Link
Lun, Xiangmin, et al. "GCNs-net: a graph convolutional neural network approach for decoding time-resolved eeg motor imagery signals." arXiv preprint arXiv:2006.08924 (2020). Link
Zhong, Peixiang, Di Wang, and Chunyan Miao. "EEG-based emotion recognition using regularized graph neural networks." IEEE Transactions on Affective Computing (2020). Link
Li, Xiaoyu, et al. "Classify EEG and reveal latent graph structure with spatio-temporal graph convolutional neural network." 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 2019. Link
Han, Jindong, et al. "GraphConvLSTM: Spatiotemporal Learning for Activity Recognition with Wearable Sensors." 2019 IEEE Global Communications Conference (GLOBECOM). IEEE, 2019. Link
Song, Tengfei, et al. "EEG emotion recognition using dynamical graph convolutional neural networks." IEEE Transactions on Affective Computing 11.3 (2018): 532-541. Link
Jang, Soobeom, Seong-Eun Moon, and Jong-Seok Lee. "EEG-based video identification using graph signal modeling and graph convolutional neural network." 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. Link
IoT Sensor Interconnection:
Shrivastava, Namita, Amit Bhagat, and Rajit Nair. "Graph Powered Machine Learning in Smart Sensor Networks." Smart Sensor Networks. Springer, Cham, 2022. 209-226. Link
Deng, Ailin, and Bryan Hooi. "Graph neural network-based anomaly detection in multivariate time series." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. No. 5. 2021. Link
Lin, Dan, et al. "Multilabel aerial image classification with a concept attention graph neural network." IEEE Transactions on Geoscience and Remote Sensing 60 (2021): 1-12. Link
Ding, Yao, et al. "Semi-supervised locality preserving dense graph neural network with ARMA filters and context-aware learning for hyperspectral image classification." IEEE Transactions on Geoscience and Remote Sensing (2021). Link
Almeida, João Damião, et al. "SENSORIMOTOR GRAPH: Action-Conditioned Graph Neural Network for Learning Robotic Soft Hand Dynamics." 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2021. Link
Fischer, Kai, et al. "StickyPillars: Robust and efficient feature matching on point clouds using graph neural networks." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021. Link
Tekbıyık, Kürşat, et al. "Graph attention networks for channel estimation in RIS-assisted satellite IoT communications." arXiv preprint arXiv:2104.00735 (2021). Link
Boyaci, Osman, et al. "Graph neural networks based detection of stealth false data injection attacks in smart grids." IEEE Systems Journal (2021). Link
Xu, Aidong, et al. "Graph-Based Time Series Edge Anomaly Detection in Smart Grid." 2021 7th IEEE Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing,(HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS). IEEE, 2021. Link
Lo, Wai Weng, et al. "E-graphsage: A graph neural network based intrusion detection system." arXiv preprint arXiv:2103.16329 (2021). Link
Wu, Yulei, Hong-Ning Dai, and Haina Tang. "Graph neural networks for anomaly detection in industrial internet of things." IEEE Internet of Things Journal (2021). Link
Ouyang, Xiaocao, et al. "Spatial-Temporal Dynamic Graph Convolution Neural Network for Air Quality Prediction." 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. Link
Ouyang, Song, and Yansheng Li. "Combining deep semantic segmentation network and graph convolutional neural network for semantic segmentation of remote sensing imagery." Remote Sensing 13.1 (2020): 119. Link
Liang, Jiali, Yufan Deng, and Dan Zeng. "A deep neural network combined CNN and GCN for remote sensing scene classification." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13 (2020): 4325-4338. Link
Zou, Xiaofeng, Kenli Li, and Cen Chen. "Multilevel Attention Based U-Shape Graph Neural Network for Point Clouds Learning." IEEE Transactions on Industrial Informatics 18.1 (2020): 448-456. Link
Narwariya, Jyoti, et al. "Graph neural networks for leveraging industrial equipment structure: An application to remaining useful life estimation." arXiv preprint arXiv:2006.16556 (2020). Link
Chen, Mengyuan, et al. "Inference for network structure and dynamics from time series data via graph neural network." arXiv preprint arXiv:2001.06576 (2020). Link
Wu, Zonghan, et al. "Connecting the dots: Multivariate time series forecasting with graph neural networks." Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2020. Link
Zhang, Yang, et al. "A multi-modal graph neural network approach to traffic risk forecasting in smart urban sensing." 2020 17th Annual IEEE international conference on sensing, communication, and networking (SECON). IEEE, 2020. Link
Zhang, Weijia, et al. "Semi-supervised hierarchical recurrent graph neural network for city-wide parking availability prediction." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 34. No. 01. 2020. Link
Zhang, Weishan, et al. "Modeling IoT equipment with graph neural networks." IEEE Access 7 (2019): 32754-32764. Link
Bi, Yin, et al. "Graph-based object classification for neuromorphic vision sensing." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019. Link
Chen, Cen, et al. "Gated residual recurrent graph neural networks for traffic prediction." Proceedings of the AAAI conference on artificial intelligence. Vol. 33. No. 01. 2019. Link
Relavant Public Dataset for GNN in IoT:
Public Datasets
Dataset used or potential helpful in GNN-related research.
Human Acitivity Recognition (HAR)
Name
Feature
Link
NTU RGB+D
RGB videos, depth map sequences, 3D skeletal data, and infrared (IR) videos
PM2.5, PM10, SO2: SO2, NO2, CO, O3, temperature, pressure (hPa), dew point temperature (degree Celsius), precipitation (mm), wind direction, wind speed (m/s), name of the air-quality monitoring site
