Phân tích mạng nâng cao với Python

In this chapter, you will learn about bipartite graphs and how they are used in recommendation systems. You will explore the GitHub dataset from the previous course, this time analyzing the underlying bipartite graph that was used to create the graph that you used earlier. Finally, you will get a chance to build the basic components of a recommendation system using the GitHub data!

In this chapter, you will use a famous American Revolution dataset to dive deeper into exploration of bipartite graphs. Here, you will learn how to create the unipartite projection of a bipartite graph, a very useful method for simplifying a complex network for further analysis. Additionally, you will learn how to use matrices to manipulate and analyze graphs - with many computing routines optimized for matrices, you'll be able to analyze many large graphs quickly and efficiently!

In this chapter, you will delve into the fundamental ways that you can analyze graphs that change over time. You will explore a dataset describing messaging frequency between students, and learn how to visualize important evolving graph statistics.

In this chapter, you will apply everything you've learned in the previous three chapters to a forum posting dataset. You will analyze the temporal changes in forum user connectivity patterns, and make visualizations of evolving graph statistics over time.