How does GNN model spatiotemporal information? A review of "Spatial-temporal Graph Neural Network" at Queen Mary University of London, a concise explanation of the spatio-temporal graph neural network method

​These powerful algorithms have gained huge interest in the past few years. However, this performance is based on the assumption of static graph structure, which limits the performance of graph neural networks when data changes over time. Sequential graph neural network is an extension of graph neural network that considers time factors.

In recent years, various sequential graph neural network algorithms have been proposed and have achieved performance superior to other deep learning algorithms in multiple time-related applications. This review discusses interesting topics related to spatiotemporal graph neural networks, including algorithms, applications, and open challenges.

Paper address: https://www.php.cn/link/1915523773b16865a73a38acc952ccda

1. Introduction

Graph Neural Network (GNN) is a type of deep learning model specifically designed to process graph-structured data. These models exploit graph topology to learn meaningful representations of the nodes and edges of the graph. Graph neural networks are an extension of traditional convolutional neural networks and have proven effective in tasks such as graph classification, node classification, and link prediction. One of the key advantages of GNNs is that they maintain good performance even as the size of the underlying graph grows, because the number of learnable parameters is independent of the number of nodes in the graph. Graph neural networks (GNN) have been widely used in various fields such as recommendation systems, drug discovery and biology, and resource allocation in autonomous systems. However, these models are limited to static graph data, where the graph structure is fixed. In recent years, time-varying graph data has attracted increasing attention, appearing in various systems and carrying valuable temporal information. Applications of time-varying graph data include multivariate time series data, social networks, audio-visual systems, etc.

In order to meet this demand, a new GNN family has emerged: spatiotemporal GNN, which takes into account the spatial and temporal dimensions of the data by learning the temporal representation of the graph structure. This paper provides a comprehensive review of state-of-the-art spatiotemporal graph neural networks. This article begins with a brief overview of different types of spatiotemporal graph neural networks and their basic assumptions. The specific algorithms used in spatiotemporal GNNs are studied in more detail, while also providing a useful taxonomy for grouping these models. The paper also provides an overview of various applications of spatiotemporal GNNs, highlighting key areas where these models have been used to achieve state-of-the-art results. Finally, challenges facing the field and future research directions are discussed. In conclusion, this review aims to provide a comprehensive and in-depth study of spatiotemporal graph neural networks, highlighting the current state of the field, the key challenges that still need to be addressed, and the exciting future possibilities of these models.

2. Algorithm

From an algorithmic perspective, spatiotemporal graph neural networks can be divided into two categories: spectrum-based and space-based. Another classification category is methods that introduce time variation: another machine learning algorithm or defining time in a graph structure.

2.1 Hybrid spatiotemporal graph neural network

The hybrid spatiotemporal graph neural network consists of two main components: spatial component and temporal component. In hybrid spatiotemporal graph neural networks, graph neural network algorithms are used to model spatial dependencies in data.

2.2 Solo-Graph Neural Network

Another way to model time in a spatio-temporal graph neural network is to define the time frame in the GNN itself. Various methods have been proposed, including: defining time as edges, inputting time as signals into GNNs, modeling time as subgraphs, and sandwiching other machine learning architectures into GNNs (Figure 2).

3. Application

3.1 Multivariable time series forecast

Processed by graph neural network Inspired by the ability of relational dependence [10], spatiotemporal graph neural networks are widely used in multivariate time series prediction. Applications include traffic forecasting, Covid forecasting, photovoltaic power consumption, RSU communications and seismic applications.

3.2 Character interaction

In machine learning and computer vision, spatiotemporal domain learning is still a very challenging problem. The main challenge is how to model interactions between objects and higher-level concepts in large spatiotemporal contexts [18]. In such a difficult learning task, it is crucial to effectively model spatial relationships, local appearance, and complex interactions and changes over time. [18] introduced a spatiotemporal graph neural network model that loops in space and time, suitable for capturing the local appearance and complex high-level interactions of different entities and objects in changing world scenes [18].

3.3 Dynamic graph representation

Sequential graph representation learning has always been considered a very important aspect in graph machine learning [15,31]. Aiming at the limitation that existing methods rely on discrete snapshots of sequence diagrams and cannot capture powerful representations, [3] proposed a dynamic graph representation learning method based on spatiotemporal graph neural networks. Furthermore, [15] now use spatiotemporal GNN to dynamically represent brain maps. Multi-target tracking Multi-target tracking in videos relies heavily on modeling the spatio-temporal interactions between targets [16]. [16] proposed a spatiotemporal graph neural network algorithm to model the spatial and temporal interactions between objects.

3.4 Sign Language Interpretation

Sign language uses a visual-manual method to convey meaning and is the main communication tool for the deaf and hard of hearing groups. To bridge the communication gap between spoken language users and sign language users, machine learning technology is introduced. Traditionally, neural machine translation has been widely adopted, but more advanced methods are needed to capture the spatial properties of sign languages. [13] proposed a sign language translation system based on spatiotemporal graph neural network, which has a strong ability to capture the spatiotemporal structure of sign language and achieved the best performance compared with the traditional neural machine translation method [13] .

3.5 Technology Growth Ranking

Understanding the growth rate of technology is a core key to the business strategy of the technology department. Additionally, forecasting the growth rates of technologies and their relationships with each other can aid business decisions in product definition, marketing strategies, and R&D. [32] proposed a prediction method for social network technology growth ranking based on spatiotemporal graph neural network.

4. Conclusion

Graph neural networks have gained tremendous interest in the past few years. These powerful algorithms extend deep learning models to non-Euclidean spaces. However, graph neural networks are limited to static graph structure assumptions, which limits the performance of graph neural networks when data changes over time. Sequential graph neural network is an extension of graph neural network that considers time factors. This article provides a comprehensive overview of spatiotemporal graph neural networks. This paper proposes a taxonomy that divides spatiotemporal graph neural networks into two categories based on time-varying methods. The wide range of applications of spatiotemporal graph neural networks are also discussed. Finally, future research directions are proposed based on the open challenges currently faced by spatiotemporal graph neural networks.

Reference materials:​​​https://www.php.cn/link/1915523773b16865a73a38acc952ccda​​

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