Why Python? Python is one of the easiest languages to learn and use, while at the same time being very powerful: It is used by many of the most highly productive professional programmers. A few of the places that use Python extensively are Google, the New York Stock Exchange, Industrial Light and Magic, ....
Network science is a thriving and increasingly important cross-disciplinary domain that focuses on the representation, analysis and modeling of complex social and technological systems as networks or graphs.
The visualization of an individual’s social network gave each friend a dot, and then connected those friends who were also friends with each other. The result was clusters (or communities) of friends. Visually impressive, these networks and clusters also help us to understand how our lives change as we age.
Note: Attendees are suggested to get their own laptop. if you are not having one, you can use the systems availiable in the lab.
In this workshop, we will focus on the python networkx library that is highly used for mining complex network datasets.
We will be covering these points in the session:
Basics about Python
Exploiting various libraries using python
Store and Process real world networks using different formats
Analyze properties of real world networks
Analyze properties of the network at node level
Identify Meso-Scale structures in real world networks such as, Facebook Network, Citation Network, etc.
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 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.
Basic Programming Skills
Introductory knowledge of Social Networks
Imaginative minds
Technical Requirement:
Install Python
install Networkx Library
Install Matplotlib Library
Install Pickle Library
Note: Attendees are suggested to get their own laptop. if you are not having one, you can use the systems availiable in the lab.
Department of Computer Science & Eng.
SRM University, Vadapalani
Research Scholar
Department of Computer Science & Eng.
IIT Ropar India
Note: Attendees are suggested to get their own laptop. if you are not having one, you can use the systems availiable in the lab.
In this session, we will talk about latest trends and how python is emerging as a significant paradigm of easy yet powerful technologies.
We will discuss about network and its dynamic properties. we will also study the prominenet features of different social networks.
After lunch we will install various necessary softwares which will be using during our hands-on session.
We will try running python scripts and implement various modules.
In this we will learn how to implement various network terminologies using networkx function like nodes, edges, edge-weight etc.
There are several network models like preferential attachement, power law, random network etc. We will learn how to actually make these models and visualize it graphically.
In this we will visualize various social networks like facebook, twitter, co-authorship network etc and analyze the community formation
In this students will be given set of problems based on python and networkx. they have to solve it in given time.