Graph Neural Networks: Basics, Codes and Simulations for AI


Basics GNNs, GNN Explainer & PyNeuraLogic through 100 + Resources: Code Implementations in Python (StellarGraph & PyG)

What you will learn

Fundamentals Graph AI using Internet of Behaviors

Basics and implementation of Graph Neural Networks

How to a create a Graph Neural Network, its training, optimization and testing

AI Graph feature learning and prediction using FastGCN, gated and mixed grain architectures.

How to derive an AI sub- graph from Graph Neural Networks

How create a Graph AI model?

Description

Graph AI carries immense potential for us to explore, connect the dots and build intelligent applications using the Internet of Behaviors (IoB). Many Graph Neural Networks achieved state-of-the-art results on both node and graph classification tasks. However, despite GNNs revolutionizing graph representation learning, there is limited understanding of their area to the students. The purpose of this course is to unfold the basics to the cutting-edge concepts and technologies in this realm.

Graphs are all around us; real-world objects are often defined in terms of their connections to other things. A set of objects, and the connections between them, are naturally expressed as a Graph Neural Network (GCN). Recent developments have increased their capabilities and expressive power. They have profound applications in the realm of AI, fake news detection, traffic prediction to recommendation systems.

This course explores and explains modern AI graph neural networks. In this course, we look at what kind of data is most naturally phrased as a graph, and some common examples. Then we explore what makes graphs different from other types of data, and some of the specialized choices we have to make when using graphs. We then build a modern GNN, walking through each of the parts of the model and gradually to state-of-the-art AI GNN models. Finally, we provide a GNN playground where you can play around with a real-world task and dataset to build a stronger intuition of how each component of an AI GNN model contributes to the predictions it makes.

The topics of this course include:

1. Introduction to Graph Machine Learning.

2. Internet of Behaviors.

3. Homographic Intelligence.

4. Graphs Basics and Eigen Centrality.

4. Graph Neural Networks.

5. Graph Attention Networks.

6. Building a Graph Neural Network

7. GNNs Predictors by Pooling Information.

8. Graph AI and its code implementations in Python.


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9. Multi- Graphs and Hyper- Graphs in AI using IoB.

10. Design Space for a GNNs.

11. Inductive Biases in GNNs.

12. Pytorch Geometric Implementations.

13. Node2Vec Feature Learning.

14. FAST GCNs.

15. Gated Graph RNNs.

16. Graph LSTMs

17. Mixed Grain Aggregators.

18. Multimodal Graph AI.

19. 100+ Resources on Graph Neural Networks

English
language

Content

Introduction to Graph Neural Networks

Graph Neural Networks and Internet of Behaviors

Introduction to Graphs as Discrete Structures

Graph Neural Networks as Relational Indices

How to model a Graph Neural Network via GCN

Graph Neural Network, Graph neural training and implemention of different layers

Graph Learning Methods in AI

Graph AI using self and multi attention neural networks

PinSage and GraphSage Networks

Optimizing graph hyperparameters and backpropagation mechanism

Graph AI using Python

Semi- Supervised Learning with Graph Neural Networks

Building Deep Neural Networks with Graphs

How the Graph AI aggregates information from the previous layers

Graph Embedding in Deep Neural Networks

To learn link prediction Node, label prediction Graph embedding in Python

Stellar Graph Library of Python

How StellarGraph integrates smoothly with Pandas and TensorFlow

Transfer Learning in Graph AI

Transfer Learning in Deep Neural Graphs using Python- GRAPHSAGE

Node2Vec Feature Learning in Graphs

Node2Vec Learning in GNNs

Graph Web Application using Python

To implement graph web application in python
Long Short Term Memory

Optimization of Graph Neural Networks

Optimization of Graph Neural Networks

Graph Mining- Large Scale AI Graphs

Graph Mining

Game Theory & AI

Game Theory & AI

Graph Generative Adversarial Network Algorithm and its Implementation

Graph Generative Adversarial Network Algorithm and its Implementation

FASTGCN and its implementation in Python

FAST GCN for Graph Neural Networks

MGCN for Graph Neural Networks

Mixed Grain Aggregators for Graph Neural Networks

Brain Computer Interfacing and Human Augmentation via Neuromorphic Computing

Human Augmentation and BCI

Neurotransmitters and Neuromodulation in Neuromorphic Computing

Neurotransmitters and Neuromodulation in Neuromorphic Computing

Neuromorphic Computing in Healthcare

How Neuromorphic computing is used in healthcare domain

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