Installation
The Endorser will work on pretty much any *nix (Linux, Mac, BSD) system with Python 3.0+.
git clone https://github.com/eth0izzle/the-endorser.git
sudo pip3 install -r requirements.txt
- Setup your LinkedIn credentials in
config.yaml
- Download ChromeDriver for your platform (requires Chrome) and place in ./drivers. Alternatively you can use PhantomJS and launch with the
--driver phantomjs
flag (note phantomjs is 8x slower). python3 the-endorser.py <profile1> <profile2>
Usage
usage: python the-endorser.py https://www.linkedin.com/in/user1 https://www.linkedin.com/in/user2
Maps out relationships between peoples endorsements on LinkedIn.
positional arguments:
profiles Space separated list of LinkedIn profile URLs to map
optional arguments:
-h, --help show this help message and exit
--config_file CONFIG_FILE
Specify the path of the config.yaml file (default:
./the-endorser/config.yaml)
--driver DRIVER Selenium WebDriver to use to parse the webpages:
chromedriver, phantomjs (default: chromedriver)
--output OUTPUT Output module to visualise the relationships: digraph,
stdout (default: digraph)
--log LOG Path of log file. None for stdout. (default: None)
--log-level LOG_LEVEL
Logging output level: DEBUG, INFO, WARNING, ERROR.
(default: INFO)
Outputs
The Endorser is “modular” in the sense that it can output and visualise the data in different ways. An output module just needs one method: def run(profiles)
Currently there is only one output module (digraph). In the future the plan is add modules for Maltego and and Plot.ly – but feel free to get involved!
Digraph
It’s best to read this from right-to-left to identify people that have arrows from multiple profiles. Square box = skill, ellipse = person.