Data science is a “concept to unify statistics, data analysis, machine learning and their related methods” in order to “understand and analyze actual phenomena” with data. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science, in particular from the subdomains of machine learning, data mining, databases, and visualization.

Using GraphSage for node predictions

Purpose of this article is to show that the 'subject' of each paper in the Cora graph can be predicted on the basis of the graph structure together with whatever features are additionally available on the nodes.

Using Laplacians for graph learning

How the Laplacian of a graph can be used for node predictions.

Community detection using NetworkX

Analysis and visualization of communities via NetworkX.

NetworkX: the essential API

NetworkX is the Python graph API you need to understand prior to venturing into graph learning. This is an overview of the essential methods.

What is a graph database?

Graph databases for the clueless.

How graphs enhance AI

A Neo4j view on how large graphs and knowledge graphs enhance machine learning and AI in general.

Graph attention networks

How the attention mechanism can be applied to graph learning.

The Cora dataset

About a dataset we use over and over again in different graph learning articles.

Node2Vec with weighted random walks

This notebook illustrates how Node2Vec can be applied to learn low dimensional node embeddings of an edge weighted graph through weighted biased random walks over the graph.

Graph Link Prediction using GraphSAGE

Explains how a graph learning task can be turned into a standard machine learning task.