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https://github.com/aveek-saha/actornet

🎥 Generate an ego network for any actor

https://github.com/aveek-saha/actornet

Science Score: 13.0%

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Keywords

actor-network ego-network egocentric network-analysis networkx social-network-analysis tmdb-api
Last synced: 5 months ago · JSON representation

Repository

🎥 Generate an ego network for any actor

Basic Info
  • Host: GitHub
  • Owner: Aveek-Saha
  • License: mit
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 1.02 MB
Statistics
  • Stars: 2
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Topics
actor-network ego-network egocentric network-analysis networkx social-network-analysis tmdb-api
Created almost 4 years ago · Last pushed over 3 years ago
Metadata Files
Readme Funding License

README.md

Actor Net

Generate an ego network for any actor. Uses TMDb to collect information about actors, an edge is formed if an actor has worked with another in a movie or TV show. Edge weights represent how many times an actor has worked with another.

How to use

Clone this repo

Clone this repository and make it your current working directory git clone https://github.com/Aveek-Saha/ActorNet.git cd ActorNet

Install prerequisites

The required libraries for this script are: 1. networkx 1. tqdm 1. urllib

Create a config file

Create a file named config.py and add the following to it:

python tmdb_api_key = "<<Your tmdb api key>>" actor_name = "<<Name of the actor>>"

Run script

Run the script and wait for it to complete, this may take a while.

python actor_net.py

Output

The final output is stored in a gml file: data/<<name of actor>>.gml. You can open this in a tool of your choice for analysis. I used Gephi for the image shown in the example.

Example

Here's an example of a graph generated with this tool. This graph is a small subset of the top 25 actors ranked by TMDb's popularity metric that have worked with Tom Holland. Even from a rudimentary analysis of this graph a few things immediately jump out. The community detection algorithm has done an okay job of splitting the nodes into actors that are primarily connected through the MCU and those either outside the MCU or those with a more diverse set of roles.

Tom holland example

Owner

  • Name: Aveek Saha
  • Login: Aveek-Saha
  • Kind: user
  • Location: Boston, MA
  • Company: @akamai

Cloud Computing, Machine Learning and Full Stack. SDE co-op @akamai. MSCS student @northeastern. Previously at @HewlettPackard, @altimetrik & @ IIT Kgp.

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