What is Spatio-temporal Graph?

Spatio-temporal is a combination of two words where the Spatio stands for space and temporal stands for time. So if any system that consists of structural relationship information of space and time can be considered a Spatio-temporal graph, in a simpler way, we can say that these graphs can make a neural network to deal with static structures and time-varying features. We can say Spatio-temporal graphs are functions of static structure and <a href="https://analyticsindiamag.com/all-about-ubers-orbit-1-1-a-time-series-modelling-tool-for-anomaly-detection/">time-varying </a>features, as following G = (V, E, Xv(t), Xe(t))

Published on January 26, 2022
In Deep Tech

A beginner’s guide to Spatio-Temporal graph neural networks

Spatio-temporal graphs are made of static structures and time-varying features, and such information in a graph requires a neural network that can deal with time-varying features of the graph. Neural networks which are developed to deal with time-varying features of the graph can be considered as Spatio-temporal graph neural networks.

By Yugesh Verma

In the recent scenario, we can witness the success of graph neural networks in a wide range of applications. Nowadays, graph neural networks are being applied to a variety of fields like NLP, time series forecasting, clustering, etc. When we apply a graph neural network to the time series data, we call it the Spatio-temporal graph neural network. In this article, we will discuss the Spatio-temporal graph neural network in detail with its applications. The major points to be discussed in this article are listed below. Table of contents What is graph data?About Spatio-temporal graphWhat are Spatio-temporal graph neural networks?Applications of Spatio-temporal graph neural networks Let’s begin with understanding what the graph data is. What is graph data? In one of

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Yugesh Verma

Yugesh is a graduate in automobile engineering and worked as a data analyst intern. He completed several Data Science projects. He has a strong interest in Deep Learning and writing blogs on data science and machine learning.