# INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging

arXiv: https://arxiv.org/abs/2302.02738 · HTML: https://mlsanity.com/paper/2302.02738/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 55 references that are in the map.
- First posted on arXiv 6 Feb 2023.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [GMAN: A Graph Multi-Attention Network for Traffic Prediction](https://mlsanity.com/paper/1911.08415/) (2019)
- [Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction](https://mlsanity.com/paper/1610.00081/) (2016)
- [Deep Learning for Spatio-Temporal Data Mining: A Survey](https://mlsanity.com/paper/1906.04928/) (2019)
- [Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction](https://mlsanity.com/paper/1802.08714/) (2018)
- [EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks](https://mlsanity.com/paper/2108.05233/) (2021)
- [Inductive Graph Neural Networks for Spatiotemporal Kriging](https://mlsanity.com/paper/2006.07527/) (2020)
- [Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Full Version](https://mlsanity.com/paper/2203.15737/) (2022)
- [Sequential Recommendation via Stochastic Self-Attention](https://mlsanity.com/paper/2201.06035/) (2022)
- [Multi-Graph Fusion Networks for Urban Region Embedding](https://mlsanity.com/paper/2201.09760/) (2022)
- [Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting](https://mlsanity.com/paper/2111.13684/) (2021)
- [Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation](https://mlsanity.com/paper/1907.08489/) (2019)
- [Generating Simple Directed Social Network Graphs for Information Spreading](https://mlsanity.com/paper/2205.02485/) (2022)
- [Modeling Heterogeneous Relations across Multiple Modes for Potential Crowd Flow Prediction](https://mlsanity.com/paper/2101.06954/) (2021)
- [Prediction of Traffic Flow via Connected Vehicles](https://mlsanity.com/paper/2007.05460/) (2020)
- [GridTuner: Reinvestigate Grid Size Selection for Spatiotemporal Prediction Models [Technical Report]](https://mlsanity.com/paper/2201.03244/) (2022)

## Built on by (most cited first, 27 of 27)

- [A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection](https://mlsanity.com/paper/2307.03759/) (2023)
- [Graph Deep Learning for Time Series Forecasting](https://mlsanity.com/paper/2310.15978/) (2023)
- [Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning](https://mlsanity.com/paper/2310.17360/) (2023)
- [Merlin: Multi-View Representation Learning for Robust Multivariate Time Series Forecasting with Unfixed Missing Rates](https://mlsanity.com/paper/2506.12459/) (2025)
- [KITS: Inductive Spatio-Temporal Kriging with Increment Training Strategy](https://mlsanity.com/paper/2311.02565/) (2023)
- [Prompt-Based Spatio-Temporal Graph Transfer Learning](https://mlsanity.com/paper/2405.12452/) (2024)
- [Graph-based Virtual Sensing from Sparse and Partial Multivariate Observations](https://mlsanity.com/paper/2402.12598/) (2024)
- [Spatial-temporal Forecasting for Regions without Observations](https://mlsanity.com/paper/2401.10518/) (2024)
- [Joint Estimation and Prediction of City-wide Delivery Demand: A Large Language Model Empowered Graph-based Learning Approach](https://mlsanity.com/paper/2408.17258/) (2024)
- [Non-Neighbors Also Matter to Kriging: A New Contrastive-Prototypical Learning](https://mlsanity.com/paper/2401.12681/) (2024)
- [DualCast: A Model to Disentangle Aperiodic Events from Traffic Series](https://mlsanity.com/paper/2411.18286/) (2024)
- [Network-wide Freeway Traffic Estimation Using Sparse Sensor Data: A Dirichlet Graph Auto-Encoder Approach](https://mlsanity.com/paper/2503.15845/) (2025)
- [STA-GANN: A Valid and Generalizable Spatio-Temporal Kriging Approach](https://mlsanity.com/paper/2508.16161/) (2025)
- [MoGERNN: An Inductive Traffic Predictor for Unobserved Locations](https://mlsanity.com/paper/2501.12281/) (2025)
- [Spatio-Temporal Graph Neural Network for Urban Spaces: Interpolating Citywide Traffic Volume](https://mlsanity.com/paper/2505.06292/) (2025)
- [Inductive Spatial Temporal Prediction Under Data Drift with Informative Graph Neural Network](https://mlsanity.com/paper/2409.13253/) (2024)
- [Generalising Traffic Forecasting to Regions without Traffic Observations](https://mlsanity.com/paper/2508.08947/) (2025)
- [AnchorGK: Anchor-based Incremental and Stratified Graph Learning Framework for Inductive Spatio-Temporal Kriging](https://mlsanity.com/paper/2512.21569/) (2025)
- [Spatio-Temporal Partial Sensing Forecast for Long-term Traffic](https://mlsanity.com/paper/2408.02689/) (2024)
- [A Survey and Benchmarking of Spatial-Temporal Traffic Data Imputation Models](https://mlsanity.com/paper/2412.04733/) (2024)
- [Inductive Spatio-Temporal Kriging with Physics-Guided Increment Training Strategy for Air Quality Inference](https://mlsanity.com/paper/2503.09646/) (2025)
- [Leakage-Free Evaluation and Distribution-Robust Spatio-Temporal Graph Learning for Inductive Kriging](https://mlsanity.com/paper/2509.23631/) (2025)
- [Spatio-temporal Multivariate Time Series Forecast with Chosen Variables](https://mlsanity.com/paper/2510.24027/) (2025)
- [A Hybrid Inductive-Transductive Network for Traffic Flow Imputation on Unsampled Locations](https://mlsanity.com/paper/2512.17984/) (2025)
- [Uniform Inductive Spatio-Temporal Kriging](https://mlsanity.com/paper/2603.05301/) (2026)
- [AdaKernel: Learning Adaptive Kernel Parameters for Spatiotemporal Graph Neural Networks](https://mlsanity.com/paper/2606.01283/) (2026)
- [A Dataset and Model for Imputing Water Surface Elevation on a Large and Extremely Sparse Spatiotemporal Graph](https://mlsanity.com/paper/2609.11580/) (2026)

## Abstract (arXiv)

Spatio-temporal kriging is an important problem in web and social applications, such as Web or Internet of Things, where things (e.g., sensors) connected into a web often come with spatial and temporal properties. It aims to infer knowledge for (the things at) unobserved locations using the data from (the things at) observed locations during a given time period of interest. This problem essentially requires inductive learning. Once trained, the model should be able to perform kriging for different locations including newly given ones, without retraining. However, it is challenging to perform accurate kriging results because of the heterogeneous spatial relations and diverse temporal patterns. In this paper, we propose a novel inductive graph representation learning model for spatio-temporal kriging. We first encode heterogeneous spatial relations between the unobserved and observed locations by their spatial proximity, functional similarity, and transition probability. Based on each relation, we accurately aggregate the information of most correlated observed locations to produce inductive representations for the unobserved locations, by jointly modeling their similarities and differences. Then, we design relation-aware gated recurrent unit (GRU) networks to adaptively capture the temporal correlations in the generated sequence representations for each relation. Finally, we propose a multi-relation attention mechanism to dynamically fuse the complex spatio-temporal information at different time steps from multiple relations to compute the kriging output. Experimental results on three real-world datasets show that our proposed model outperforms state-of-the-art methods consistently, and the advantage is more significant when there are fewer observed locations. Our code is available at https://github.com/zhengchuanpan/INCREASE.