Network analysis using Python

Mohammed Kashif (~mohdkashif93)


12

Votes

Description:

In this short tutorial we will be exploring graph networks from the ones mentioned below and work on analysing it various properties and features which will help us to analyse the various patterns that may exist in a network. We will exploring :

  • Community detection in a network
  • Identifying nodes of influence
  • Graph properties like betweeness, centrality, transitivity, clustering coefficients, etc. and what information do they provide about the graph
  • Path finding in a network ( If time permits, we will try to take an image of a maze and find the shortest path out of the maze, by using CV and networkx)
  • Graph Databases in Python
  • Analyzing graphs based on the no. of cliques, k-cliuqes, etc.
  • Visualizing graphs in 2D and 3D space using Python

We will be covering the following libraries in this tutorial

We will be using the following graph data for our analysis:

Bonus: If time permits we will take up a random image of a maze and try finding the path out of it, something similar to this (we will be using scikit-image for skeletonizing and networkx for path finding)

enter image description here

Prerequisites:

Basic knowledge for Python will suffice

Content URLs:

Will update this repository in a few days to include sample notebooks : https://github.com/mohdkashif93/PyCon-Graph-Analysis

In the meantime you can checkout these repositories for reference

Speaker Info:

Hi, I am a Python Developer at Qualcomm, who is super enthusiastic about comics and video games. Sometimes when I get bored I head over to Stackoverflow and solve other people's problems, which is my version of being the friendly neighbourhood spiderman (or Nagraj, since Python translates loosely to Naag or snake in Hindi, so you know... sorry that was a lame reference) :)

Speaker Links:

Stackoverflow : https://stackoverflow.com/users/story/8160718
Github : https://github.com/mohdkashif93
LinkedIn : https://www.linkedin.com/in/mohdkashif93/

Section: Data science
Type: Talks
Target Audience: Beginner
Last Updated:

Not bad.

Anand B Pillai (~pythonhacker)

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