Automated Spatio-Temporal Graph Contrastive Learning
- URL: http://arxiv.org/abs/2305.03920v1
- Date: Sat, 6 May 2023 03:52:33 GMT
- Title: Automated Spatio-Temporal Graph Contrastive Learning
- Authors: Qianru Zhang, Chao Huang, Lianghao Xia, Zheng Wang, Zhonghang Li and
Siuming Yiu
- Abstract summary: 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.
- Score: 18.245433428868775
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Among various region embedding methods, graph-based region relation learning
models stand out, owing to their strong structure representation ability for
encoding spatial correlations with graph neural networks. Despite their
effectiveness, several key challenges have not been well addressed in existing
methods: i) Data noise and missing are ubiquitous in many spatio-temporal
scenarios due to a variety of factors. ii) Input spatio-temporal data (e.g.,
mobility traces) usually exhibits distribution heterogeneity across space and
time. In such cases, current methods are vulnerable to the quality of the
generated region graphs, which may lead to suboptimal performance. In this
paper, we tackle the above challenges by exploring the Automated
Spatio-Temporal graph contrastive learning paradigm (AutoST) over the
heterogeneous region graph generated from multi-view data sources. Our \model\
framework is built upon a heterogeneous graph neural architecture to capture
the multi-view region dependencies with respect to POI semantics, mobility flow
patterns and geographical positions. To improve the robustness of our GNN
encoder against data noise and distribution issues, we design an automated
spatio-temporal augmentation scheme with a parameterized contrastive view
generator. AutoST can adapt to the spatio-temporal heterogeneous graph with
multi-view semantics well preserved. Extensive experiments for three downstream
spatio-temporal mining tasks on several real-world datasets demonstrate the
significant performance gain achieved by our \model\ over a variety of
baselines. The code is publicly available at https://github.com/HKUDS/AutoST.
Related papers
- Spatial-temporal Memories Enhanced Graph Autoencoder for Anomaly Detection in Dynamic Graphs [52.956235109354175]
Anomaly detection in dynamic graphs presents a significant challenge due to the temporal evolution of graph structures and attributes.
We introduce a novel Spatial-Temporal memories-enhanced graph autoencoder (STRIPE)
STRIPE has demonstrated a superior capability to discern anomalies by effectively leveraging the distinct spatial and temporal dynamics of dynamic graphs.
arXiv Detail & Related papers (2024-03-14T02:26:10Z) - 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) - Graph Neural Processes for Spatio-Temporal Extrapolation [36.01312116818714]
We study the task of extrapolation-temporal processes that generates data at target locations from surrounding contexts in a graph.
Existing methods either use learning-grained models like Neural Networks or statistical approaches like Gaussian for this task.
We propose Spatio Graph Neural Processes (STGNP), a neural latent variable model which commands these capabilities simultaneously.
arXiv Detail & Related papers (2023-05-30T03:55:37Z) - Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic
Prediction [1.6449390849183363]
We propose an automated dilated-temporal synchronous graph network prediction named Auto-DSTS for traffic prediction.
Specifically, we propose an automated dilated-temporal-temporal graph (Auto-DSTS) module to capture the short-term and long-term-temporal correlations.
Our model can achieve about 10% improvements compared with the state-of-art methods.
arXiv Detail & Related papers (2022-07-22T00:50:39Z) - Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs [65.18780403244178]
We propose a continuous model to forecast Multivariate Time series with dynamic Graph neural Ordinary Differential Equations (MTGODE)
Specifically, we first abstract multivariate time series into dynamic graphs with time-evolving node features and unknown graph structures.
Then, we design and solve a neural ODE to complement missing graph topologies and unify both spatial and temporal message passing.
arXiv Detail & Related papers (2022-02-17T02:17:31Z) - Spatio-Temporal Joint Graph Convolutional Networks for Traffic
Forecasting [75.10017445699532]
Recent have shifted their focus towards formulating traffic forecasting as atemporal graph modeling problem.
We propose a novel approach for accurate traffic forecasting on road networks over multiple future time steps.
arXiv Detail & Related papers (2021-11-25T08:45:14Z) - Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network [39.65520262751766]
We develop a new traffic prediction framework-Spatial-Temporal Graph Diffusion Network (ST-GDN)
In particular, ST-GDN is a hierarchically structured graph neural architecture which learns not only the local region-wise geographical dependencies, but also the spatial semantics from a global perspective.
Experiments on several real-life traffic datasets demonstrate that ST-GDN outperforms different types of state-of-the-art baselines.
arXiv Detail & Related papers (2021-10-08T11:19:06Z) - SST-GNN: Simplified Spatio-temporal Traffic forecasting model using
Graph Neural Network [2.524966118517392]
We have designed a simplified S-temporal GNN(SST-GNN) that effectively encodes the dependency by separately aggregating different neighborhood.
We have shown that our model has significantly outperformed the state-of-the-art models on three real-world traffic datasets.
arXiv Detail & Related papers (2021-03-31T18:28:44Z) - Spatial-spectral Hyperspectral Image Classification via Multiple Random
Anchor Graphs Ensemble Learning [88.60285937702304]
This paper proposes a novel spatial-spectral HSI classification method via multiple random anchor graphs ensemble learning (RAGE)
Firstly, the local binary pattern is adopted to extract the more descriptive features on each selected band, which preserves local structures and subtle changes of a region.
Secondly, the adaptive neighbors assignment is introduced in the construction of anchor graph, to reduce the computational complexity.
arXiv Detail & Related papers (2021-03-25T09:31:41Z) - Spatio-Temporal Graph Scattering Transform [54.52797775999124]
Graph neural networks may be impractical in some real-world scenarios due to a lack of sufficient high-quality training data.
We put forth a novel mathematically designed framework to analyze-temporal data.
arXiv Detail & Related papers (2020-12-06T19:49:55Z) - Connecting the Dots: Multivariate Time Series Forecasting with Graph
Neural Networks [91.65637773358347]
We propose a general graph neural network framework designed specifically for multivariate time series data.
Our approach automatically extracts the uni-directed relations among variables through a graph learning module.
Our proposed model outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets.
arXiv Detail & Related papers (2020-05-24T04:02:18Z)
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.