(testing signal)

Tag: GNN

What are graph neural networks (GNN)?

Graphs are everywhere around us. Your social network is a graph of people and relations. So is your family. The roads you take to go from point A to point B constitute a graph. The links that connect this webpage to others form a graph. When your employer pays you, your payment goes through a graph of financial institutions.
Basically, anything that is composed of…… Read more...

Fuse Graph Neural Networks with Semantic Reasoning to Produce Complimentary Predictions

Photo by Maxime VALCARCE on Unsplash
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Graph Neural Network (GNN) Architectures for Recommendation Systems

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Machine Learning on Graphs, Part 1

Photo by Alina Grubnyak on Unsplash

Collecting basic statistics

In a series of posts, I will provide an overview of several machine learning approaches to learning from graph data. Starting with basic statistics that are used to describe graphs, I will go deeper into the subject by discussing node embeddings, graph kernels, graph signal processing, and eventually graph neural networks. The posts are intended to reflect on my personal experience in academia and industry, including some of my research papers. My main motivation is to present first some basic approaches to machine learning on graphs that should be used before digging into advanced algorithms like graph neural networks.… Read more...

Graph Neural Networks Combined with Semantic Reasoning Deliver ‘Total AI’

The ability for machines to reason—not just identify patterns in massive data amounts, but make rule or logic based inferences on domain specific knowledge—is foundational to Artificial Intelligence. The growing momentum around Neuro-Symbolic AI and the increasing reliance on Graph Analytics demonstrate how important these developments are for the enterprise.

Combining AI’s symbolic knowledge processing with its statistical branch (typified by machine learning) produces the best prescriptive outcomes, delivers total AI, and is swiftly becoming necessary to tackle enterprise scale applications of mission-critical processes like foretelling equipment failure, optimizing healthcare treatment, and maximizing customer relationships.

Graph Neural Networks (GNN) exemplify the confluence of machine learning and AI reasoning.

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Let’s Talk About Graph Neural Network Python Libraries!

Since I want to keep it simple, I will use the popular Zachary’s Karate Club graph dataset. Here, the nodes represent 34 students who were involved in the club and the links represent 78 different interactions between pairs of members outside the club. There are two different types of labels i.e, the two factions.

Node Classification: In this task, our aim is to build a model to predict the labels of the nodes i.e, the factions joined by the students.

We divide the graph into train and test sets (in the ratio 70:30) using a specific seed value.

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A Beginner’s Guide to Graph Neural Networks Using PyTorch Geometric — Part 2

Our idea is to capture the network information using an array of numbers which are called low-dimensional embeddings. There exist different algorithms specifically for the purpose of learning numerical representations for graph nodes.

DeepWalk is a node embedding technique that is based on the Random Walk concept which I will be using in this example. In order to implement it, I picked the Graph Embedding python library that provides 5 different types of algorithms to generate the embeddings.

Firstly, install the Graph Embedding library and run the setup:

!git clone https://github.com/shenweichen/GraphEmbedding.gitcd GraphEmbedding/!python setup.py install

We use the DeepWalk model to learn the embeddings for our graph nodes.

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A Beginner’s Guide to Graph Neural Networks Using PyTorch Geometric — Part 1

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