## An Illustrated Guide to Graph Neural Networks

*Graph Deep Learning (GDL)* is an up-and-coming area of study. It’s
super useful when learning over and analysing graph data. Here, I’ll
cover the basics of a simple *Graph Neural Network* (GNN) and the
intuition behind its inner workings.

Source: An Illustrated Guide to Graph Neural
Networks,
an article by Rishabh Anand.

## What is Benford’s Law and why is it important for data science?

We discuss a little-known gem for data analytics — Benford’s law,
which tells us about expected distribution of significant digits in
a diverse set of naturally occurring datasets and how this can be
used for anomaly or fraud detection in scientific or technical
publications.

Source: What is Benford’s Law and why is it important for data
science?,
an article by Tirthajyoti Sarkar.

## A Guide to Big O notation

You have probably come across big O notation before. Maybe you have
read that merge sort is better than insertion sort because merge
sort is *O*(*n* log *n*) compared to insertion sort, which is
*O*(*n*^{2}). In this article, you'll understand what this
means, and why this makes merge sort the better algorithm.

Source: A Guide to Big O
notation, an article by Erik André Jakobsen.