August  Larson

August Larson


How to Analyze Marvel vs DC Data Using Python

Marvel Cinematic vs DC Universe, it’s a never-ending debate, right? Fans got crazy when you oppose any of these cinematic universes. But in the article, we are going to do a fight over Marvel vs DC based on some data. Data always tells the truth. You can write the Python code in Jupyter Notebook, Google Colab, or any other preferred editor. I will recommend you Google Colab because I use it more often.

#marvel #data-analysis #python 

How to Analyze Marvel vs DC Data Using Python

Avanya Shina


Exploring Marvel API with Python

In this video i will show you how you work on marvel api using python


#python #marvel

Exploring Marvel API with Python

From CSV to GitHub Pages in 5 Steps: Publishing an Interactive Social Network of Marvel

How to easily create an awesome graph visualization using Python, Gephi, and GitHub Pages

In this article, I will provide a step-by-step guide on how you can create, publish, and share interactive network graph visualizations in 5 simple steps.

Before we get started, here are the three things we need:

  • Python & NetworkX library [ 1]
  • Gephi [ 2] & SigmaExporter package
  • GitHub account & a public GitHub repository

Step 1: Import the CSV file and create a NetworkX graph

Step 2: Export the NetworkX graph as gexf file

Step 3: Import and modify the graph in Gephi

Step 4: Export the graph as Sigma JS

Step 5: Publish the graph visualization on GitHub pages

#network #data #marvel #github

From CSV to GitHub Pages in 5 Steps: Publishing an Interactive Social Network of Marvel
Virgil  Hagenes

Virgil Hagenes


Exploratory network analysis of Marvel Universe

A wise man once said that the 2020–30 decade will be the decade of graph data science. Actually, that happened just a few days ago at the Nodes 2020 conference, and that wise man was Emil Eifrem presenting at the keynote of the Nodes 2020. In case you missed the conference, all the presentations are already available online.

Only fitting Emil’s statement, a pre-release of the 1.4 version of the Neo4j Graph Data Science library was published a couple of days ago. It is a significant milestone for the GDS library. A lot of new features were added in this release. If you are interested to learn more, you can inspect the release statement. In this blog post, we will take a look at the new k-Nearest Neighbours algorithm. But before we do that, we will do a proper graph exploratory analysis.

I’ve written so many blog posts it takes an effort to find some excellent datasets I haven’t explored yet. This time I saw a cool repository on Kaggle containing data about the Marvel Universe. Unfortunately, I later realized that only comics and characters files have matching ids. As a graph analyst, we want to connect all the relevant data, and without matching ids, that’s a bit hard. I then realized that those matching ids were scraped from the Marvel API. I fetched some additional data from the API to enrich our graph. The information about the characters is not available over the API but is available on their website. This made me put on my Spider-Man suit and test my web crawling abilities. I’m quite proud to say that I learned to use Selenium and efficiently scraped the information about the characters from the Marvel website.

Graph import

You can easily import this Marvel Universe graph by running the cypher statements from this gist. It contains ten simple LOAD CSV cypher statements and an apoc.schema.assert procedure to define unique constraints and indexes. If you use Neo4j Browser, make sure you have enabled the multi-statement query editor.

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This way, you can copy the content of the whole gist and not worry about executing each statement separately.

Graph schema

Now that we have imported the graph, we can examine its schema with the following procedure:

CALL db.schema.visualization()

If you run this procedure in Neo4j Browser, you will get this nice visualization of the graph schema.

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In the center of the graph, there are characters, also known as heroes. They can appear in multiple comics, are part of an event, and can belong to a group. For some of the characters, we also know their stats like speed and fighting skills. Finally, we have social ties between characters that represent relative, ally, or enemy relationships.

To get a feel for the size of the graph, we can run the following APOC procedure:

CALL apoc.meta.stats() YIELD labels
return labels

#marvel #data-science #algorithms

Exploratory network analysis of Marvel Universe

How To Get Started with Social Network Analysis

This guide is intended to get complete beginners in social network analysis up to speed on terminology and concepts to create and analyze their first network graph.
This is a long article. Feel free to bookmark this article and come back to it from time to time as you are first learning social network analysis. An outline of this article is also provided below:

#social-network-analysis #data-visualization #guides-and-tutorials #marvel #data-science

How To Get Started with Social Network Analysis
Dhiraj Ray

Dhiraj Ray


Best Design Courses and Tutorials

#photoshop #design #adobe #marvel

Best Design Courses and Tutorials