Graph Masked Autoencoder for Spatio-Temporal Graph Learning

• URL: http://arxiv.org/abs/2410.10915v1
• Date: Mon, 14 Oct 2024 07:33:33 GMT
• Title: Graph Masked Autoencoder for Spatio-Temporal Graph Learning
• Authors: Qianru Zhang, Haixin Wang, Siu-Ming Yiu, Hongzhi Yin,
• Abstract summary: In urban sensing applications, effective-temporal prediction frameworks play a crucial role in traffic analysis, human mobility evaluations and crime prediction. The presence of data noise and sparsity in spatial and temporal data presents significant challenges for existing neural network models in learning robust representations. We propose a novel self-supervised learning paradigm for effective-temporal data augmentation.
• Score: 38.085962443141206
• License:
• Abstract: Effective spatio-temporal prediction frameworks play a crucial role in urban sensing applications, including traffic analysis, human mobility behavior modeling, and citywide crime prediction. However, the presence of data noise and label sparsity in spatio-temporal data presents significant challenges for existing neural network models in learning effective and robust region representations. To address these challenges, we propose a novel spatio-temporal graph masked autoencoder paradigm that explores generative self-supervised learning for effective spatio-temporal data augmentation. Our proposed framework introduces a spatial-temporal heterogeneous graph neural encoder that captures region-wise dependencies from heterogeneous data sources, enabling the modeling of diverse spatial dependencies. In our spatio-temporal self-supervised learning paradigm, we incorporate a masked autoencoding mechanism on node representations and structures. This mechanism automatically distills heterogeneous spatio-temporal dependencies across regions over time, enhancing the learning process of dynamic region-wise spatial correlations. To validate the effectiveness of our STGMAE framework, we conduct extensive experiments on various spatio-temporal mining tasks. We compare our approach against state-of-the-art baselines. The results of these evaluations demonstrate the superiority of our proposed framework in terms of performance and its ability to address the challenges of spatial and temporal data noise and sparsity in practical urban sensing scenarios.

Related papers

• Kriformer: A Novel Spatiotemporal Kriging Approach Based on Graph Transformers [5.4381914710364665]
  This study addresses posed by sparse sensor deployment and unreliable data by framing the problem as an environmental challenge. A graphkriformer model, Kriformer, estimates data at locations without sensors by mining spatial and temporal correlations, even with limited resources.
  arXiv  Detail & Related papers  (2024-09-23T11:01:18Z)
• SFANet: Spatial-Frequency Attention Network for Weather Forecasting [54.470205739015434]
  Weather forecasting plays a critical role in various sectors, driving decision-making and risk management. Traditional methods often struggle to capture the complex dynamics of meteorological systems. We propose a novel framework designed to address these challenges and enhance the accuracy of weather prediction.
  arXiv  Detail & Related papers  (2024-05-29T08:00:15Z)
• Spatio-Temporal Branching for Motion Prediction using Motion Increments [55.68088298632865]
  Human motion prediction (HMP) has emerged as a popular research topic due to its diverse applications. Traditional methods rely on hand-crafted features and machine learning techniques. We propose a noveltemporal-temporal branching network using incremental information for HMP.
  arXiv  Detail & Related papers  (2023-08-02T12:04:28Z)
• Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation [19.419836274690816]
  We propose a new spatial-temporal graph learning model (GraphST) for enabling effective self-supervised learning. Our proposed model is an adversarial contrastive learning paradigm that automates the distillation of crucial multi-view self-supervised information. We demonstrate the superiority of our proposed GraphST method in various spatial-temporal prediction tasks on real-life datasets.
  arXiv  Detail & Related papers  (2023-06-19T03:09:35Z)
• Automated Spatio-Temporal Graph Contrastive Learning [18.245433428868775]
  We develop an automated-temporal augmentation scheme with a parameterized contrastive view generator. AutoST can adapt to the heterogeneous graph with multi-view semantics well preserved. Experiments for three downstream-temporal mining tasks on several real-world datasets demonstrate the significant performance gain.
  arXiv  Detail & Related papers  (2023-05-06T03:52:33Z)
• An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning [14.353798949041698]
  We propose an adaptive federated relevance framework, namely FedRel, for spatial-temporal graph learning. The core Dynamic Inter-Intra Graph (DIIG) module in the framework is able to use these features to generate the spatial-temporal graphs. To improve the model generalization ability and performance while preserving the local data privacy, we also design a relevance-driven federated learning module.
  arXiv  Detail & Related papers  (2022-06-07T16:12:17Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• Learning Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)
• On the spatial attention in Spatio-Temporal Graph Convolutional Networks for skeleton-based human action recognition [97.14064057840089]
  Graphal networks (GCNs) promising performance in skeleton-based human action recognition by modeling a sequence of skeletons as a graph. Most of the recently proposed G-temporal-based methods improve the performance by learning the graph structure at each layer of the network.
  arXiv  Detail & Related papers  (2020-11-07T19:03:04Z)
• A Novel Framework for Spatio-Temporal Prediction of Environmental Data Using Deep Learning [0.0]
  We introduce here a framework for decomposed-temporal prediction of climate and environmental data using deep learning. Specifically, we introduce functions which can be spatially and mapped on a regular grid allowing the reconstruction of complete-temporal-signal. Applications on simulated real-world data will show the effectiveness of the proposed framework.
  arXiv  Detail & Related papers  (2020-07-23T07:44:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.