Network Visualization - An Introduction

A brief introduction to the second chapter.

In this video, we dive into the details of network science and discuss the core concepts of networks. I explain the different components of a network, such as nodes and links, and how they are interconnected. I also touch on important concepts like paths and network diameter.

In this video, we further discuss key concepts of network science by diving into network centralities, such as NodeDegree and PageRank, and explain how they quantify the importance of nodes in a network. I also touch on clustering and network communities.

A brief introduction to the third chapter.

In this video, we provide an overview of how to place an abstract network onto a 2D plane and visualize networks. We will learn about network layouts and various visualization tools, and touch base on the primary tool of this course - Gephi.

In this video, we create our first network visualization. First, we will read a previously prepared CSV spreadsheet containing the edge list of a dummy network. Then, we will explore the core parts of Gephi (overview, data table, and preview), adjust the network visualization (layout, node coloring, sizing, node labels), and create and export the final visual in the preview. Both the exported final image and the Gephi file are also attached here.

Here, we quickly overview how the Witcher network was created.

In this video, we start from the final, properly designed Witcher network shown in the previous presentation, and we deconstruct the graph by removing each visual attribute step by step.

In this video, we re-design the Witcher network from scratch. We learn how to load a .gexf file in Gephi, explore different network layouts and learn how to color and size nodes based on their properties. We will also cover network statistics, such as computing network communities and node centralities, and use those to fine-tune the final network visualization.

A brief introduction to the fourth chapter.

In this video, we go through my network, visualizing the hidden connections behind the Manhattan Project using Gephi. We will ruin a nicely designed visualization and then rebuild it by changing graph layout, node colors, sizing, labeling, and many more. We will also interact with the overview-preview tabs back and forth to collect some more hands-on experience with it.

In this video, I introduce a network visualization of the connections between Nobel laureates. Based on a dataset acquired from Wikipedia, I show how each laureate is linked to others based on shared references. The strength of the connections is determined by the frequency of shared references. Throughout the video, I demonstrate the steps to visualize and analyze the network, highlighting important features such as field of study, number of views, and multiple prizes.

This last video will be a bit more experimental - while the previously shown networks were all published and well-curated graphs, for this video, I visualized a brand-new graph I finished building right before the recording. So this is the first time I saw this network, visualizing the shared usage network of Python libraries in my data science tutorials. Hence, in this video, you will get to see live how I improvize and fine-tune a visualization of network data I see for the first time.