1. Congratulations!
Congratulations on finishing the course! I hope you've learned a ton from the material presented here. At the beginning of this course, you built upon your previous knowledge and first tackled the topic of
2. What you’ve learned
bipartite graphs - graphs that have nodes divided into two partitions. For example, the user-forum graph from chapter 4, the user-repository graph from chapter 1, and the toy customer-product graph throughout the videos, are examples of bipartite graphs. You learned how to compute bipartite graph statistics, such as degree centrality, using the NetworkX package as well. Next up, you learned about
3. What you’ve learned
projections of bipartite graphs. The projection of a bipartite graph is the connectivity between the nodes in one partition, conditioned on their connections to nodes in the other partition. This is useful for simplifying the view of a bipartite graph, and lets you use algorithms available for unipartite graphs, some of which you learned previously. Next up, you learned about
4. What you’ve learned
manipulating graphs in their matrix form! This is a very useful topic to know - it'll enable you to handle very large graphs locally. Matrix operations that we touched on here include matrix multiplication and transposition; in the context of bipartite graphs, they let you compute the projection efficiently. There's a world of matrix operations as applied to graphs out there, and what you've learned here will give you a great foundation for learning more! Finally, you learned about the foundations underlying
5. What you’ve learned
time-series analysis of graph data. At its core, time-series analysis is about the analysis of edge changes in a graph; the easiest representation for this is to use graph differences, available as part of the NetworkX API.
6. Let's practice!
I hope this course has given you a solid foundation for learning new things network-related in the future, and I wish you all the best in your future learning journey!