Description:

In this workshop, we will focus on the python networkx library that is highly used to mine complex network datasets.

We will be covering these points in the session:

1. Store and Process real world networks using different formats

2. Analyze properties of real world networks

3. Analyze properties of the network at node level

4. Identify Meso-Scale structures in real world networks such as, Facebook Network, Citation Network, etc.

5. Epidemic Models to explain Information diffusion, opinion dynamics, and so on.

Motivation:

In this workshop, we will motivate students towards the use of python in network science and how we can make some quick inferences from real world complex datasets using networkx library. We will cover all the functions provided by the library and how these functions can be modified a little bit to get more information with less effort. We will also be explaining, what all properties can be studied using inbuilt functions, and when it is required to write your own code with the help of given library. We will also include a small component to explain the comparison of networkx library with other available libraries, so that users can pick the best one based on their requirements.

In the end, we will share some sample codes that will help to analyze networks structure, its properties, and dynamic phenomenon taking place on real world networks, like how information diffusion happens in real world networks and how it can be visualized with a small piece of code. Some more examples like: How we can detect community structure using partial information of the network, how we can analyze the correlation of network properties, and so on. As we have also observed with interns (specially undergraduate students), they are not aware of these quick libraries and how these libraries can help them in getting good understanding of science happening in complex networks. Through this workshop, We would like to motivate them and to explain them that the handling of real world networks is not so complex and even a small piece of code can help them getting better results. We will also share real world datasets so that attendees can use it to perform analysis. Based on the interest of attendees, we are also open to share the code of our research experiments and their results.

Prerequisites:

1. Basic Python Programming Skills

2. Introductory knowledge of Social Networks

3. Imaginative minds

Technical Requirement:

1. Install Python

2. install Networkx Library

3. Install Matplotlib Library

4. Install Pickle Library

Content URLs:

Slides: https://docs.google.com/presentation/d/1bSY0NRpSfRhTZu-LOd9kr9YxBsaNFXoiPvYSVqC4cuk/edit#slide=id.gc6f919934_0_0

Detailed Proposal: https://docs.google.com/document/d/1SdhfIBGnG4EP0M6LyQnQrh2LeFkFtn86vawPqSxl1kw/edit

Speaker Info:

Akrati Saxena is a research scholar (PhD) in CSE dept at IIT Ropar. She is working in network science (Social Networks and Complex Networks) from the past two and a half years. She has published papers in many International conferences based on Network Science. She has also worked as a software developer in Newgen Software Tech. Ltd. Delhi for two years. After that She joined State Bank Group as an IT Officer where She developed various softwares that are used in processing financial data.

Speaker Links:

Homepage: https://sites.google.com/a/iitrpr.ac.in/akrati/home

Google Scholar Profile: https://scholar.google.co.in/citations?user=HXHLTIgAAAAJ&hl=en

ResearchGate Profile: https://www.researchgate.net/profile/Akrati_Saxena

Linkedin Profile: https://www.linkedin.com/in/akratiiet

Resume: https://docs.google.com/document/d/1Qhz4Sr9idlIrsaeaRptXoVvpJySGhqNabBjLNYvhdSk/edit