This document introduces network analysis in Python. It discusses how networks can model relationships between entities and provide insights like important nodes, efficient paths, and communities. It introduces NetworkX for working with graphs and shows examples of creating graphs, adding nodes and edges, and getting basic information. It also discusses different types of graphs like directed, undirected, and multigraphs. Finally, it covers visualizing networks using matrix plots, arc plots, and Circos plots and shows an example of creating an arc plot using the nxviz API.

1. NETWORK ANALYSIS IN PYTHON I
Introduction
to networks

2. Network Analysis in Python I
Networks!
● Examples:
● Social
● Transportation
● Model relationships between entities

3. Network Analysis in Python I
Networks!
● Insights:
● Important entities: influencers in social network
● Pathfinding: most efficient transport path
● Clustering: finding communities

4. Network Analysis in Python I
Network structure
Node
Node
Edge
Graph

5. Network Analysis in Python I
Network structure
Hugo:
id: 1,
age: 34
Friendship:
date: 2016-05-21
Social
Graph
Eric:
id: 2,
age: 29

6. Network Analysis in Python I
NetworkX API basics
In [1]: import networkx as nx
In [2]: G = nx.Graph()
In [4]: G.add_nodes_from([1, 2, 3])
In [5]: G.nodes()
Out[5]: [1, 2, 3]
In [6]: G.add_edge(1, 2)
In [7]: G.edges()
Out[7]: [(1, 2)]

7. Network Analysis in Python I
NetworkX API basics
In [8]: G.node[1]['label'] = 'blue'
In [9]: G.nodes(data=True)
Out[9]: [(1, {'label': 'blue'}), (2, {}), (3, {})]

8. Network Analysis in Python I
NetworkX API basics
In [10]: nx.draw(G)
In [11]: import matplotlib.pyplot as plt
In [12]: plt.show()

9. NETWORK ANALYSIS IN PYTHON I
Let’s practice!

10. NETWORK ANALYSIS IN PYTHON
Types of graphs

11. Network Analysis in Python I
● Facebook social graph
Undirected graphs

12. Network Analysis in Python I
Undirected graphs
In [1]: import networkx as nx
In [2]: G = nx.Graph()
In [3]: type(G)
Out[3]: networkx.classes.graph.Graph

13. Network Analysis in Python I
● Directed: Twi er social graph
Directed graphs

14. Network Analysis in Python I
Directed graphs
In [4]: D = nx.DiGraph()
In [5]: type(D)
Out[5]: networkx.classes.digraph.DiGraph

15. Network Analysis in Python I
● Multi(Di)Graph: Trip records between bike sharing stations
Types of graphs

16. Network Analysis in Python I
Multi-edge (Directed) graphs
In [6]: M = nx.MultiGraph()
In [7]: type(M)
Out[7]: networkx.classes.multigraph.MultiGraph
In [8]: MD = nx.MultiDiGraph()
In [9]: type(MD)
Out[9]: networkx.classes.multidigraph.MultiDiGraph

17. Network Analysis in Python I
● Edges can contain weights
Weights on graphs
3

18. Network Analysis in Python I
● Nodes that are connected to themselves
Self-loops

19. NETWORK ANALYSIS IN PYTHON I
Let’s practice!

20. NETWORK ANALYSIS IN PYTHON I
Network
visualization

21. Network Analysis in Python I
Irrational vs. Rational visualizations

22. Network Analysis in Python I
Visualizing networks
● Matrix plots
● Arc plots
● Circos plots

23. Network Analysis in Python I
Visualizing networks
● Matrix plots
● Arc plots
● Circos plots

24. Network Analysis in Python I
Matrix plot
A B C
A
B
C
A
B
C

25. Network Analysis in Python I
Matrix plot
A B C
A
B
C
A
B
C

26. Network Analysis in Python I
Matrix plot
A B C
A
B
C
A
B
C

27. Network Analysis in Python I
Matrix plot
A B C
A
B
C
A
B
C

28. Network Analysis in Python I
Directed matrices
A B C
A
B
C
A
B
C

29. Network Analysis in Python I
Visualizing networks
● Matrix Plots
● Arc Plots
● Circos Plots

30. Network Analysis in Python I
Arc plot
A B C
A
B
C
ordered axis

31. Network Analysis in Python I
Visualizing networks
● Matrix Plots
● Arc Plots
● Circos Plots

32. Network Analysis in Python I
Circos plot
A
C
B
D
E
F
A
C
B
D
E
F

33. Network Analysis in Python I
nxviz API
In [1]: import nxviz as nv
In [2]: import matplotlib.pyplot as plt
In [3]: ap = nv.ArcPlot(G)
In [4]: ap.draw()
In [5]: plt.show()

34. NETWORK ANALYSIS IN PYTHON I
Let’s practice!