A Short Review on Graph Neural Networks: Mechanism and Implementation

Updated on September 14, 2022 | in 2. Knowledge Network / Network Science

Graphical Neural Networks (GNNs) are a family of neural networks that can operate naturally on graphically structured data. They provide an effective way to do node-level, edge-level, and graph-level prediction tasks by extracting and exploiting features from the underlying graph.

Project | Optimize Supportive Vector Machine and Least Square Regression Models for Traffic Flow Prediction

Updated on October 22, 2021 | in Data Science

This paper adopted Supportive Vector Regression (SVR) and Least Square Regression(LSR) models to predict the traffic flow. LSR model and SVR models with linear, Gaussian, polynomial kernel separately are built and then evaluated with MSE and R-squared.

Study Notes | Stanford CS520: Knowledge Graph

Updated on August 10, 2021 | in 2. Knowledge Network / Network Science

Notes are shared publicly on github.

A Short Review on Graph Neural Networks: Mechanism and Implementation

Project | Optimize Supportive Vector Machine and Least Square Regression Models for Traffic Flow Prediction

Study Notes | Stanford CS520: Knowledge Graph

Junyi Tao