Article Source

• A Survey on Graph Neural Networks for Time Series

A Survey on Graph Neural Networks for Time Series

• Temporal Graph Learning Reading Group
• Papers: “A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection “
• Speaker: Ming Jin
• Date: Oct. 12, 2023

Related Papers

• Article Source: Awesome Graph Neural Networks for Time Series Analysis (GNN4TS)

GNNs for Time Series Forecasting (GNN4TSF)

• Diffusion convolutional recurrent neural network: Data-driven traffic forecasting (ICLR, 2018) [paper]
• Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting (IJCAI, 2018) [paper]
• Urban traffic prediction from spatio-temporal data using deep meta learning (KDD, 2019) [paper]
• Autoregressive Models for Sequences of Graphs (IEEE IJCNN, 2019) [paper]
• ST-UNet: A Spatio-Temporal U-Network forGraph-structured Time Series Modeling (arXiv, 2019) [paper]
• Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting (AAAI, 2019) [paper]
• Graph Attention Recurrent Neural Networks for Correlated Time Series Forecasting (MileTS, 2019) [paper]
• Attention Based Spatial-Temporal Graph Convolutional Networksfor Traffic Flow Forecasting (AAAI, 2019) [paper]
• Spatiotemporal Multi-Graph Convolution Network for Ride-hailing Demand Forecasting (AAAI, 2019) [paper]
• Graph wavenet for deep spatial-temporal graph modeling (IJCAI, 2019) [paper]
• STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting (IJCAI, 2019) [paper]
• Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting (AAAI, 2020) [paper]
• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks (KDD, 2020) [paper]
• Traffic Flow Prediction via Spatial Temporal Graph Neural Network (WWW, 2020) [paper]
• Towards Fine-grained Flow Forecasting: A Graph Attention Approach for Bike Sharing Systems (WWW, 2020) [paper]
• GMAN: A Graph Multi-Attention Network for Traffic Prediction (AAAI, 2020) [paper]
• Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting (AAAI, 2020) [paper]
• Spatio-Temporal Graph Structure Learning for Traffic Forecasting (AAAI, 2020) [paper]
• Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting (NeurIPS, 2020) [paper]
• Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting (NeurIPS, 2020) [paper]
• GraphSleepNet: Adaptive Spatial-Temporal Graph Convolutional Networks for Sleep Stage Classification (IJCAI, 2020) [paper]
• LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks (IJCAI, 2020) [paper]
• ST-GRAT: A Novel Spatio-temporal Graph Attention Network for Accurately Forecasting Dynamically Changing Road Speed (CIKM, 2020) [paper]
• Spatiotemporal Hypergraph Convolution Network for Stock Movement Forecasting (ICDM, 2020) [paper]
• Forecaster: A Graph Transformer for Forecasting Spatial and Time-Dependent Data (ECAI, 2020) [paper]
• Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction (ECCV, 2020) [paper]
• Discrete Graph Structure Learning for Forecasting Multiple Time Series (ICLR, 2021) [paper]
• MTHetGNN: A heterogeneous graph embedding framework for multivariate time series forecasting (Pattern Recognition, 2021) [paper]
• Graph Edit Networks (ICLR, 2021) [paper]
• Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting (ICML, 2021) [paper]
• Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting (KDD, 2021) [paper]
• Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting (AAAI, 2021) [paper]
• Hierarchical Graph Convolution Network for Traffic Forecasting (AAAI, 2021) [paper]
• Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network (AAAI, 2021) [paper]
• TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learning (IJCAI, 2021) [paper]
• DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting (ICML, 2022) [paper]
• Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks (NeurIPS, 2022) [paper]
• Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities (CIKM, 2022) [paper]
• Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs (IEEE TKDE, 2022) [paper]
• Graph Neural Controlled Differential Equations for Traffic Forecasting (AAAI, 2022) [paper]
• CausalGNN: Causal-Based Graph Neural Networks for Spatio-Temporal Epidemic Forecasting (AAAI, 2022) [paper]
• Auto-STGCN: Autonomous Spatial-Temporal Graph Convolutional Network Search (ACM TKDD, 2022) [paper]
• TAMP-S2GCNets: Coupling Time-Aware Multipersistence Knowledge Representation with Spatio-Supra Graph Convolutional Networks for Time-Series Forecasting (ICLR, 2022) [paper]
• Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting (KDD, 2022) [paper]
• Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting (KDD, 2022) [paper]
• Mining Spatio-Temporal Relations via Self-Paced Graph Contrastive Learning (KDD, 2022) [paper]
• Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting (IJCAI, 2022) [paper]
• Long-term Spatio-Temporal Forecasting via Dynamic Multiple-Graph Attention (IJCAI, 2022) [paper]
• FOGS: First-Order Gradient Supervision with Learning-based Graph for Traffc Flow Forecasting (IJCAI, 2022) [paper]
• METRO: A Generic Graph Neural Network Framework for Multivariate Time Series Forecasting (VLDB, 2022) [paper]
• Scalable Spatiotemporal Graph Neural Networks (AAAI, 2023) [paper]
• Graph State-Space Models (arXiv, 2023) [paper]
• Graph Kalman Filters (arXiv, 2023) [paper]
• Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting (arXiv, 2023) [paper]
• Adaptive Graph Spatial-Temporal Transformer Network for Traffic Flow Forecasting (arXiv, 2022) [paper]
• DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models (arXiv, 2023) [paper]
• How Expressive are Spectral-Temporal Graph Neural Networks for Time Series Forecasting? (arXiv, 2023) [paper]
• Spatio-Temporal Meta-Graph Learning for Traffic Forecasting (AAAI, 2023) [paper]
• Temporal Graph Neural Networks for Irregular Data (AISTATS, 2023) [paper]
• Adaptive Spatiotemporal Transformer Graph Network for Traffic Flow Forecasting by IoT Loop Detectors (IEEE Internet of Things Journal, 2023) [paper]

GNNs for Time Series Classification (GNN4TSC)

• Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets (AAAI, 2020) [paper]
• Towards Similarity-Aware Time-Series Classification (SDM, 2022) [paper]
• Multivariate Time Series Classification with Hierarchical Variational Graph Pooling (Neural Network, 2022) [paper]
• Graph-Guided Network for Irregularly Sampled Multivariate Time Series (ICLR, 2022) [paper]
• Time2Graph+: Bridging Time Series and Graph Representation Learning via Multiple Attentions (TKDE, 2023) [paper]
• LB-SimTSC: An Efficient Similarity-Aware Graph Neural Network for Semi-Supervised Time Series Classification (DLG-AAAI, 23) [paper]
• An Empirical Study of Graph-Based Approaches for Semi-Supervised Time Series Classification (arXiv, 2023) [paper]
• TodyNet: Temporal Dynamic Graph Neural Network for Multivariate Time Series Classification (arXiv, 2023) [paper]

GNNs for Time Series Anomaly Detection (GNN4TAD)

• Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds (IEEE TNNLS, 2019)[paper]
• Multivariate time-series anomaly detection via graph attention network (ICDM, 2020) [paper]
• Graph Neural Network-Based Anomaly Detection in Multivariate Time Series (AAAI, 2021) [paper]
• Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoT (IEEE IoT, 2021) [paper]
• Time-Series Event Prediction with Evolutionary State Graph (WSDM, 2021) [paper]
• Consistent anomaly detection and localization of multivariate time series via cross-correlation graph-based encoder–decoder gan. (IEEE TIM, 2021) [paper]
• Graph-augmented normalizing flows for anomaly detection of multiple time series (ICLR, 2022) [paper]
• Grelen:Multivariate time series anomaly detection from the perspective of graph relational learning (IJCAI, 2022) [paper]
• Deep variational graph convolutional recurrent network for multivariate time series anomaly detection. (ICML, 2022) [paper]
• Learning Sparse Latent Graph Representations for Anomaly Detection in Multivariate Time Series (KDD, 2022) [paper]
• GTAD: Graph and Temporal Neural Network for Multivariate Time Series Anomaly Detection (Entropy, 2022) [paper]
• Hypergraph Learning based Recommender System for Anomaly Detection, Control and Optimization (IEEE Big Data, 2023) [paper]
• Hybrid anomaly detection via multihead dynamic graph attention networks for multivariate time series (IEEE Access, 2022) [paper]
• Graph convolutional adversarial networks for spatiotemporal anomaly detection. (IEEE TNNLS, 2022) [paper]
• Graph iForest: Isolation of anomalous and outlier graphs (IEEE IJCNN, 2022)[[paper]] (https://ieeexplore.ieee.org/document/9892295)
• Multivariate Time Series Anomaly Detection via Dynamic Graph Forecasting (arXiv, 2023) [paper]
• Time Series Subsequence Anomaly Detection via Graph Neural Networks (OpenReview, 2023) [paper]
• VARIATIONAL ADAPTIVE GRAPH TRANSFORMER FOR MULTIVARIATE TIME SERIES MODELING (OpenReview, 2023) [paper]
• A CAUSAL APPROACH TO DETECTING MULTIVARIATE TIME-SERIES ANOMALIES AND ROOT CAUSES (arXiv, 2023) [paper]

GNNs for Time Series Imputation (GNN4TSI)

• Inductive Graph Neural Networks for Spatiotemporal Kriging (AAAI, 2021) [paper]
• Spatial-temporal traffic data imputation via graph attention convolutional network (ICANN, 2021) [paper]
• Spatial Aggregation and Temporal Convolution Networks for Real-time Kriging (arXiv, 2021) [paper]
• Dynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic Data Imputation with Complex Missing Patterns (arXiv, 2021) [paper]
• Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks (ICLR, 2022) [paper]
• Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations (NeurIPS, 2022) [paper]
• Forecasting Unobserved Node States With Spatiotemporal Graph Neural Networks (ICDMW, 2022) [paper]
• Adaptive Graph Recurrent Network for Multivariate Time Series Imputation (ICONIP, 2022) [paper]
• A Multi-Attention Tensor Completion Network for Spatiotemporal Traffic Data Imputation (IEEE Intenet of Things Journal, 2022) [paper]
• PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation (arXiv, 2023) [paper]
• Dynamic graph convolutional recurrent imputation network for spatiotemporal traffic missing data (Knowledge-Based Systems, 2023) [paper]
• Bidirectional spatial–temporal traffic data imputation via graph attention recurrent neural network (Neuralcomputing, 2023) [paper]
• Memory-augmented dynamic graph convolution networks for traffic data imputation with diverse missing patterns (Transportation Research Part C, 2023) [paper]
• Large-Scale Traffic Data Imputation with Spatiotemporal Semantic Understanding (arXiv, 2023) [paper]

📚 Applications

Healthcare

• GraphConvLSTM: Spatiotemporal Learning for Activity Recognition with Wearable Sensors (GLOBECOM, 2019) [paper]
• Graph temporal ensembling based semi-supervised convolutional neural network with noisy labels for histopathology image analysis (Medical Image Analysis, 2020) [paper]
• GraphSleepNet: Adaptive Spatial-Temporal Graph Convolutional Networks for Sleep Stage Classification (IJCAI, 2020) [paper]
• Graph neural network-based diagnosis prediction (Big Data, 2020) [paper]
• A hybrid method of recurrent neural network and graph neural network for next-period prescription prediction (International Journal of Machine Learning and Cybernetics, 2020) [paper]
• GATE: graph-attention augmented temporal neural network for medication recommendation (Access, 2020) [paper]
• Knowledge guided diagnosis prediction via graph spatial-temporal network (SDM, 2020) [paper]
• Multi-view spatial-temporal graph convolutional networks with domain generalization for sleep stage classification (TNSRE, 2021) [paper]
• Interpretable temporal graph neural network for prognostic prediction of Alzheimer’s disease using longitudinal neuroimaging data (BIBM, 2021) [paper]
• Forecasting ambulance demand with profiled human mobility via heterogeneous multi-graph neural networks (ICDE, 2021) [paper]
• Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis (ICLR, 2022) [paper]
• CausalGNN: Causal-Based Graph Neural Networks for Spatio-Temporal Epidemic Forecasting (AAAI, 2022) [paper]
• Temporal Multiresolution Graph Neural Networks For Epidemic Prediction (arXiv, 2022) [paper]
• Graph-Guided Network for Irregularly Sampled Multivariate Time Series (ICLR, 2022) [paper]
• Spatio-temporal fusion attention: A novel approach for remaining useful life prediction based on graph neural network (TIM, 2022) [paper]
• Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly COVID-19 cases in Germany (Scientific Reports, 2022) [paper]
• Using Ubiquitous Mobile Sensing and Temporal Sensor-Relation Graph Neural Network to Predict Fluid Intake of End Stage Kidney Patients (IPSN, 2022) [paper]
• Predicting COVID-19 pandemic by spatio-temporal graph neural networks: A New Zealand’s study (arXiv, 2022) [paper]
• Self-supervised learning for anomalous channel detection in eeg graphs: Application to seizure analysis (AAAI, 2023) [paper]

Smart Transportation

• Diffusion convolutional recurrent neural network: Data-driven traffic forecasting (ICLR, 2018) [paper]
• Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting (IJCAI, 2018) [paper]
• Urban traffic prediction from spatio-temporal data using deep meta learning (KDD, 2019) [paper]
• Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting (AAAI, 2020) [paper]
• Traffic Flow Prediction via Spatial Temporal Graph Neural Network (WWW, 2020) [paper]
• GMAN: A Graph Multi-Attention Network for Traffic Prediction (AAAI, 2020) [paper]
• Spatio-Temporal Graph Structure Learning for Traffic Forecasting (AAAI, 2020) [paper]
• Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting (NeurIPS, 2020) [paper]
• ST-GRAT: A Novel Spatio-temporal Graph Attention Network forAccurately Forecasting Dynamically Changing Road Speed (CIKM, 2020) [paper]
• Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting (KDD, 2021) [paper]
• Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting (AAAI, 2021) [paper]
• Hierarchical Graph Convolution Network for Traffic Forecasting (AAAI, 2021) [paper]
• TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learning (IJCAI, 2021) [paper]
• Dynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic Data Imputation with Complex Missing Patterns (arXiv, 2021) [paper]
• A deep learning approach for flight delay prediction through time-evolving graphs (IEEE TITS, 2021) [paper]
• Dynstgat: Dynamic spatial-temporal graph attention network for traffic signal control (CIKM, 2021) [paper]
• DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting (ICML, 2022) [paper]
• Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities (CIKM, 2022) [paper]
• Graph Neural Controlled Differential Equations for Traffic Forecasting (AAAI, 2022) [paper]
• FOGS: First-Order Gradient Supervision with Learning-based Graph for Traffc Flow Forecasting (IJCAI, 2022) [paper]
• Adaptive Graph Spatial-Temporal Transformer Network for Traffic Flow Forecasting (arXiv, 2022) [paper]
• A Multi-Attention Tensor Completion Network for Spatiotemporal Traffic Data Imputation (IEEE Internet of Things Journal, 2022) [paper]
• SGDAN: A spatio-temporal graph dual-attention neural network for quantified flight delay prediction (Sensors, 2022) [paper]
• Spatio-Temporal Meta-Graph Learning for Traffic Forecasting (AAAI, 2023) [paper]
• Dynamic graph convolutional recurrent imputation network for spatiotemporal traffic missing data (Knowledge-Based Systems, 2023) [paper]
• Bidirectional spatial–temporal traffic data imputation via graph attention recurrent neural network (Neuralcomputing, 2023) [paper]
• Memory-augmented dynamic graph convolution networks for traffic data imputation with diverse missing patterns (Transportation Research Part C, 2023) [paper]
• STGSA: A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction (IEEE/CAA Journal of Automatica Sinica, 2023) [paper]
• Spatial–Temporal Complex Graph Convolution Network for Traffic Flow Prediction (Engineering Applications of Artificial Intelligence, 2023) [paper]
• Long Short-Term Fusion Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting (Electronics, 2023) [paper]
• Traffic Flow Forecasting in the COVID-19: A Deep Spatial-Temporal Model Based on Discrete Wavelet Transformation (ACM TKDD, 2023) [paper]
• Trajectory prediction for autonomous driving based on multiscale spatial‐temporal graph (IET Intelligent Transport Systems, 2023) [paper]
• Predictive Neural Motion Planner for Autonomous Driving Using Graph Networks (IEEE TIV, 2023) [paper]
• SAT-GCN: Self-attention graph convolutional network-based 3D object detection for autonomous driving (Knowledge-Based Systems, 2023) [paper]
• Large-Scale Traffic Data Imputation with Spatiotemporal Semantic Understanding (arXiv, 2023) [paper]

On-Demand Services

• Deep multi-view spatial-temporal network for taxi demand prediction (AAAI, 2018) [paper]
• Spatiotemporal Multi-Graph Convolution Network for Ride-hailing Demand Forecasting (AAAI, 2019) [paper]
• STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting (IJCAI, 2019) [paper]
• Spatio-temporal graph convolutional and recurrent networks for citywide passenger demand prediction (CIKM, 2019) [paper]
• Graph convolutional network approach applied to predict hourly bike-sharing demands considering spatial, temporal, and global effects (PLOS ONE, 2019) [paper]
• Towards Fine-grained Flow Forecasting: A Graph Attention Approach for Bike Sharing Systems (WWW, 2020) [paper]
• Multi-task spatial-temporal graph attention network for taxi demand prediction (ICMAI, 2020) [paper]
• Residential Electric Load Forecasting via Attentive Transfer of Graph Neural Networks (IJCAI, 2021) [paper]
• A comparative study of using spatial-temporal graph convolutional networks for predicting availability in bike sharing schemes (ITS, 2021) [paper]
• Multi-community passenger demand prediction at region level based on spatio-temporal graph convolutional network (Transportation Research Part C, 2021) [paper]
• Demand prediction for a public bike sharing program based on spatio-temporal graph convolutional networks (Multimedia Tools and Applications, 2021) [paper]
• Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networks (KDD, 2022) [paper]
• Graph2Route: A Dynamic Spatial-Temporal Graph Neural Network for Pick-up and Delivery Route Prediction (kDD, 2022) [paper]
• Short-term prediction of bike-sharing demand using multi-source data: a spatial-temporal graph attentional LSTM approach (Applied Sciences, 2022) [paper]
• Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction (Computers, Environment and Urban Systems, 2022) [paper]
• A data-driven spatial-temporal graph neural network for docked bike prediction (ICDE, 2022) [paper]
• Multi-Attribute Spatial-temporal Graph Convolutional Network for Taxi Demand Forecasting (ICBDT, 2022) [paper]
• A graph-attention based spatial-temporal learning framework for tourism demand forecasting (Knowledge-Based Systems, 2023) [paper]
• On region-level travel demand forecasting using multi-task adaptive graph attention network (Information Sciences, 2023) [paper]

Environment & Sustainable Energy

• Spatio-temporal graph deep neural network for short-term wind speed forecasting (IEEE TSTE, 2018) [paper]
• M2GSNet: Multi-Modal Multi-Task Graph Spatiotemporal Network for Ultra-Short-Term Wind Farm Cluster Power Prediction (Applied Sciences, 2020) [paper]
• Superposition graph neural network for offshore wind power prediction (FGCS, 2020) [paper]
• Spatio-temporal graph neural networks for multi-site PV power forecasting (IEEE TSTE, 2021) [paper]
• Spatiotemporal graph neural network for performance prediction of photovoltaic power systems (AAAI, 2021) [paper]
• A graph neural network based deep learning predictor for spatio-temporal group solar irradiance forecasting (IEEE TII, 2021) [paper]
• Forecasting PM2. 5 using hybrid graph convolution-based model considering dynamic wind-field to offer the benefit of spatial interpretability (Environmental Pollution, 2021) [paper]
• Forecasting Global Weather with Graph Neural Networks (arXiv, 2022) [paper]
• Promoting wind energy for sustainable development by precise wind speed prediction based on graph neural networks (Renewable Energy, 2022) [paper]
• Short-term wind power prediction via spatial temporal analysis and deep residual networks (Frontiers in Energy Research, 2022) [paper]
• A robust spatio‐temporal prediction approach for wind power generation based on spectral temporal graph neural network (IET Renewable Power Generation, 2022) [paper]
• Interpretable deep learning models for hourly solar radiation prediction based on graph neural network and attention (Applied Energy, 2022) [paper]
• A new ensemble spatio-temporal PM2. 5 prediction method based on graph attention recursive networks and reinforcement learning (Chaos, Solitons & Fractals, 2022) [paper]
• Short-term wind speed forecasting based on spatial-temporal graph transformer networks (Energy, 2022) [paper]
• Optimal Graph Structure based Short-term Solar PV Power Forecasting Method Considering Surrounding Spatio-temporal Correlations (IEEE TIA, 2022) [paper]
• Condition monitoring of wind turbines with the implementation of spatio-temporal graph neural network (Engineering Applications of Artificial Intelligence, 2023) [paper]
• Cost-effective fault diagnosis of nearby photovoltaic systems using graph neural networks (Energy, 2023) [paper]
• Maximising Weather Forecasting Accuracy through the Utilisation of Graph Neural Networks and Dynamic GNNs (arXiv, 2023) [paper]
• Spatiotemporal air pollution forecasting in houston-TX: a case study for ozone using deep graph neural networks (Atmosphere, 2023) [paper]

Internet of Things

• Joint interaction and trajectory prediction for autonomous driving using graph neural network (arXiv, 2019) [paper]
• Semi-supervised hierarchical recurrent graph neural network for city-wide parking availability prediction. (AAAI, 2020) [paper]
• Multiscale Spatio-Temporal Graph Neural Networks for 3D Skeleton-Based Motion Prediction (IEEE-TIP, 2021) [paper]
• Making Offensive Play Predictable - Using a Graph Convolutional Network to Understand Defensive Performance in Soccer (2021) [paper]
• Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoT (IEEE IoT, 2021) [paper]
• Graph Neural Networks for Anomaly Detection in Industrial Internet of Things (IEEE IoT, 2021) [paper]
• Attentional-gcnn: Adaptive pedestrian trajectory pre- diction towards generic autonomous vehicle use cases (IEEE ICRA, 2021) [paper]
• Dignet: Learning scalable self-driving policies for generic traffic scenarios with graph neural network (IEEE IROS, 2021) [paper]
• Detection of tactical patterns using semi-supervised graph neural networks (MIT Sloan Sports Analytics Conference, 2022) [paper]
• Who You Play Affects How You Play: Predicting Sports Performance Using Graph Attention Networks With Temporal Convolution (arXiv, 2023) [paper]

Fraud Detection

• TIES: Temporal Interaction Embeddings For Enhancing Social Media Integrity At Facebook (KDD, 2020) [paper]
• Early Anomaly Detection by Learning and Forecasting Behavior (arXiv, 2021) [paper]
• Recurrent Graph Neural Networks for Rumor Detection in Online Forums (arXiv, 2021) [paper]
• Temporal Graph Representation Learning for Detecting Anomalies in E-payment Systems (ICDMW, 2021) [paper]
• Temporal-aware graph neural network for credit risk prediction (arXiv, 2021) [paper]
• TeGraF: temporal and graph based fraudulent transaction detection framework (ICAIF, 2021) [paper]
• What Happens Behind the Scene? Towards Fraud Community Detection in E-Commerce from Online to Offline (WWW, 2021) [paper]
• Temporal Debiasing using Adversarial Loss based GNN architecture for Crypto Fraud Detection (ICMLA, 2021) [paper]
• APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding (SIGMOD/PODS, 2021) [paper]
• Graph Neural Networks in Real-Time Fraud Detection with Lambda Architecture (arXiv, 2021) [paper]
• Graph Neural Network for Fraud Detection via Spatial-Temporal Attention (TKDE, 2022) [paper]
• A Novel Spatiotemporal Prediction Approach Based on Graph Convolution Neural Networks and Long Short-Term Memory for Money Laundering Fraud (Arabian Journal for Science and Engineering, 2022) [paper]
• Towards Fine-Grained Reasoning for Fake News Detection (AAAI, 2022) [paper]
• Modelling graph dynamics in fraud detection with “Attention” (arXiv, 2022) [paper]
• TTAGN:Temporal Transaction Aggregation Graph Network For Ethereum Phishing Scams Detection (WWW, 2022) [paper]
• Multi-view Heterogeneous Temporal Graph Neural Network for “Click Farming” Detection (PRICAI, 2022) [paper]
• BRIGHT - Graph Neural Networks in Real-time Fraud Detection (CIKM, 2022) [paper]
• Exploiting Spatial-Temporal Behavior Patterns for Fraud Detection in Telecom Networks (TDSC, 2022) [paper]
• Medical Insurance Fraud Detection using Graph Neural Networks with Spatio-temporal Constraints (Journal of Network Intelligence, 2022) [paper]