Network Graph Analysis with Python

Complex systems, such as a power grid, the World Wide Web, activity in different regions of the brain, or people within a community, can be understood, studied and visualized based on their connections in a network. In this article, I will introduce you to a data science project on network graph analysis with Python.

Introduction to Network Graphs

Network Graph Analysis has real broad applications in the field of networking. Two main areas are involved in the analysis of the application of network graphs, which are a graph-based representation and network theory.

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A graph has two components which are nodes and edges. In a graphical representation, these components have natural correspondences with the elements of the problem. In general, nodes in a graph represent features and edges represent interactions between features.

Most complex systems are problems of a graphical type, such as the transportation network, the trade links of an industry within a country, the genetic interaction network, and the Internet. On the other hand, network theory works as a tool that provides a set of techniques to analyze a graph and apply network theory using a graphical representation.

Network Graph Analysis with Python

For this network graph analysis task with Python, I will be using data from the tags used by Stack Overflow. The dataset I’m using here contains network links, source and target technical tags, and the link value between each pair.

It also contains the nodes of the network, the name of each node, the group to which this node belongs and a node size based on the frequency of use of this technological beacon. So let’s see how to visualize and analyze a network graph using the Python programming language:

Network Graph Analysis

I hope you liked this article on Network Graph Analysis with Python programming language. Feel free to ask your valuable questions in the comments section below.

Aman Kharwal
Aman Kharwal

I'm a writer and data scientist on a mission to educate others about the incredible power of data📈.

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