Properties of bipartite adjacency matrices.

Let's recap concepts about bipartite graphs and their adjacency matrices.

Which of the following statements best describes bipartite adjacency matrices?

Questo esercizio fa parte del corso

Intermediate Network Analysis in Python

Visualizza il corso

Esercizio pratico interattivo

Passa dalla teoria alla pratica con uno dei nostri esercizi interattivi

Inizia esercizio

Questo esercizio fa parte del corso

Intermediate Network Analysis in Python

AvançadoNível de habilidade

4.8+

Inizia il corso gratis

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!

Exercise 1: Definitions & basic recap Exercise 2: Exploratory data analysis Exercise 3: Plotting using nxviz Exercise 4: Bipartite graphs Exercise 5: The bipartite keyword Exercise 6: Degree centrality distribution of user nodes Exercise 7: Degree centrality distribution of project nodes Exercise 8: Bipartite graphs and recommendation systems Exercise 9: Shared nodes in other partition Exercise 10: User similarity metric Exercise 11: Find similar users Exercise 12: Recommend repositories

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!

Exercise 1: Concept of projection Exercise 2: Reading graphs Exercise 3: Computing projection Exercise 4: Plot degree centrality on projection Exercise 5: Bipartite graphs as matrices Exercise 6: Properties of bipartite adjacency matrices.

Esercizio in corso

Exercise 7: Compute adjacency matrix Exercise 8: Find shared membership: Transposition Exercise 9: Representing network data with pandas Exercise 10: Make nodelist Exercise 11: Make edgelist

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.

Exercise 1: Introduction to graph differences Exercise 2: List of graphs Exercise 3: Graph differences over time Exercise 4: Plot number of edge changes over time Exercise 5: Evolving graph statistics Exercise 6: Number of edges over time Exercise 7: Degree centrality over time Exercise 8: Zooming in & zooming out: Overall graph summary Exercise 9: Find nodes with top degree centralities Exercise 10: Visualizing connectivity

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.

Exercise 1: Introduction to the dataset Exercise 2: Create a graph from the pandas DataFrame Exercise 3: Visualize the degree centrality distribution of the students projection Exercise 4: Visualize the degree centrality distribution of the forums projection Exercise 5: Time based filtering Exercise 6: Time filter on edges Exercise 7: Visualize filtered graph using nxviz Exercise 8: Time series analysis Exercise 9: Plot number of posts being made over time Exercise 10: Extract the mean degree centrality day-by-day on the students partition Exercise 11: Find the most popular forums day-by-day: I Exercise 12: Find the most popular forums day-by-day: II Exercise 13: Congratulations!