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PROJECT


Link-Based Classification Using Graph Neural Networks

In this project, we'll create a Graph Neural Network (GNN) using the PyTorch Geometric framework, and we'll use it to classify the scientific publications of the Cora dataset.

Link-Based Classification Using Graph Neural Networks

You will learn to:

Plot data using dedicated Python libraries.

Understand the fundamentals of graph neural networks.

Get hands-on experience with the PyTorch Geometric framework.

Solve a classification problem using graph neural networks.

Skills

Deep Learning

Data Plotting

Graph Neural Networks

Prerequisites

Basic understanding of deep learning models

Hands-on experience with Python

Technologies

PyG logo

PyG

Python

PyTorch

Matplotlib

Project Description

Graph neural networks (GNNs) are a class of deep learning models that capture intrinsic data patterns to facilitate model training. They are finding extensive applications in social and ego networks, molecular discovery, and other domains where the data has an underlying graph structure.

In this project, we will develop a graph convolutional network (GCN) to classify the scientific publications in the Cora dataset. As the Cora dataset consists of interlinked data, using GNNs will allow us to capture more data correlations as compared to conventional neural networks for improved model performance. We will import the Cora dataset, implement the graph convolutional network, and use it to classify the scientific publications in the Cora dataset. Moreover, we will analyze the model performance for different split ratios of the dataset.

Project Tasks

1

Getting Started

Task 0: Introduction

Task 1: Import Libraries

Task 2: Load the Dataset

2

Create a Graph Neural Network

Task 3: Initialize the Graph Neural Network

Task 4: Create a forward() Function

3

Define Training and Evaluation Functions

Task 5: Define the Training Function

Task 6: Define the Evaluation Function

Task 7: Modularize the Training Process

4

Classify the Graph Nodes

Task 8: Train the GCN Model

Task 9: Plot the Results

Task 10: Evaluate the Model for Different Split Ratios

Congratulations!