Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer

• URL: http://arxiv.org/abs/2205.13947v1
• Date: Fri, 27 May 2022 12:46:52 GMT
• Title: Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer
• Authors: Bin Lu, Xiaoying Gan, Weinan Zhang, Huaxiu Yao, Luoyi Fu, Xinbing Wang
• Abstract summary: Cross-city knowledge has shown its promise, where the model learned from data-sufficient cities is leveraged to benefit the learning process of data-scarce cities. We propose a model-agnostic few-shot learning framework for S-temporal graph called ST-GFSL. We conduct comprehensive experiments on four traffic speed prediction benchmarks and the results demonstrate the effectiveness of ST-GFSL compared with state-of-the-art methods.
• Score: 58.6106391721944
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Spatio-temporal graph learning is a key method for urban computing tasks, such as traffic flow, taxi demand and air quality forecasting. Due to the high cost of data collection, some developing cities have few available data, which makes it infeasible to train a well-performed model. To address this challenge, cross-city knowledge transfer has shown its promise, where the model learned from data-sufficient cities is leveraged to benefit the learning process of data-scarce cities. However, the spatio-temporal graphs among different cities show irregular structures and varied features, which limits the feasibility of existing Few-Shot Learning (\emph{FSL}) methods. Therefore, we propose a model-agnostic few-shot learning framework for spatio-temporal graph called ST-GFSL. Specifically, to enhance feature extraction by transfering cross-city knowledge, ST-GFSL proposes to generate non-shared parameters based on node-level meta knowledge. The nodes in target city transfer the knowledge via parameter matching, retrieving from similar spatio-temporal characteristics. Furthermore, we propose to reconstruct the graph structure during meta-learning. The graph reconstruction loss is defined to guide structure-aware learning, avoiding structure deviation among different datasets. We conduct comprehensive experiments on four traffic speed prediction benchmarks and the results demonstrate the effectiveness of ST-GFSL compared with state-of-the-art methods.

Related papers

• Spatio-Temporal Few-Shot Learning via Diffusive Neural Network Generation [25.916891462152044]
  We propose a novel generative pre-training framework, GPD, for intricate few-shot learning with urban knowledge transfer. We recast a generative diffusion model, which generates tailored neural networks guided by prompts. GPD consistently outperforms state-of-the-art baselines on datasets for tasks such as traffic speed prediction and crowd flow prediction.
  arXiv  Detail & Related papers  (2024-02-19T08:11:26Z)
• Spatio-Temporal Meta Contrastive Learning [18.289397543341707]
  We propose a new-temporal contrastive learning framework to encode robust and generalizable S-temporal Graph representations. We show that our framework significantly improves performance over various state-of-the-art baselines in traffic crime prediction.
  arXiv  Detail & Related papers  (2023-10-26T04:56:31Z)
• 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)
• Semantic-Fused Multi-Granularity Cross-City Traffic Prediction [17.020546413647708]
  We propose a Semantic-Fused Multi-Granularity Transfer Learning model to achieve knowledge transfer across cities with fused semantics at different granularities. In detail, we design a semantic fusion module to fuse various semantics while conserving static spatial dependencies. We conduct extensive experiments on six real-world datasets to verify the effectiveness of our STL model.
  arXiv  Detail & Related papers  (2023-02-23T04:26:34Z)
• Localized Contrastive Learning on Graphs [110.54606263711385]
  We introduce a simple yet effective contrastive model named Localized Graph Contrastive Learning (Local-GCL) In spite of its simplicity, Local-GCL achieves quite competitive performance in self-supervised node representation learning tasks on graphs with various scales and properties.
  arXiv  Detail & Related papers  (2022-12-08T23:36:00Z)
• Spatio-Temporal Latent Graph Structure Learning for Traffic Forecasting [6.428566223253948]
  We propose a new traffic forecasting framework--S-Temporal Latent Graph Structure Learning networks (ST-LGSL) The model employs a graph based on Multilayer perceptron and K-Nearest Neighbor, which learns the latent graph topological information from the entire data. With the dependencies-kNN based on ground-truth adjacency matrix and similarity metric in kNN, ST-LGSL aggregates the top focusing on geography and node similarity.
  arXiv  Detail & Related papers  (2022-02-25T10:02:49Z)
• Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos [78.45050529204701]
  We propose a novel framework to pursue discriminative and robust representation by modeling cross-scale spatial-temporal correlation. CTL utilizes a CNN backbone and a key-points estimator to extract semantic local features from human body. It explores a context-reinforced topology to construct multi-scale graphs by considering both global contextual information and physical connections of human body.
  arXiv  Detail & Related papers  (2021-04-15T14:32:12Z)
• Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning [64.98816284854067]
  Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a handful of labeled data to the remaining massive unlabeled data via a graph. A novel GCN-based SSL algorithm is presented in this paper to enrich the supervision signals by utilizing both data similarities and graph structure.
  arXiv  Detail & Related papers  (2020-09-15T13:59:28Z)
• Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting [88.5550074808201]
  We propose Geographic and Long term Temporal Graph Convolutional Recurrent Neural Network (GLT-GCRNN) for traffic forecasting. In this work, we propose a novel framework for traffic forecasting that learns the rich interactions between roads sharing similar geographic or longterm temporal patterns.
  arXiv  Detail & Related papers  (2020-04-23T03:50:46Z)
• Tensor Graph Convolutional Networks for Multi-relational and Robust Learning [74.05478502080658]
  This paper introduces a tensor-graph convolutional network (TGCN) for scalable semi-supervised learning (SSL) from data associated with a collection of graphs, that are represented by a tensor. The proposed architecture achieves markedly improved performance relative to standard GCNs, copes with state-of-the-art adversarial attacks, and leads to remarkable SSL performance over protein-to-protein interaction networks.
  arXiv  Detail & Related papers  (2020-03-15T02:33:21Z)

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.