Network Analysis with NetworkX

In this series of lessons, we’re going to learn about network analysis. Network analysis will help us better understand the complex relationships between groups of people, fictional characters, and other kinds of things.

Dataset

Game of Thrones

Lucky it might be, and red it certainly was, but Ygritte’s hair was such a tangle that Jon was tempted to ask her if she only brushed it at the changing of the seasons.

-George R. R. Martin, A Song of Ice and Fire

Network Basics

Install NetworkX

!pip install networkx

Requirement already satisfied: networkx in /Users/melaniewalsh/opt/anaconda3/lib/python3.7/site-packages (2.4)
Requirement already satisfied: decorator>=4.3.0 in /Users/melaniewalsh/opt/anaconda3/lib/python3.7/site-packages (from networkx) (4.4.2)

Import Libraries

import networkx
import pandas as pd
pd.set_option('max_rows', 400)
import matplotlib.pyplot as plt

Game of Thrones Network

The network data that we’re going to use in this lesson is taken from Andrew Beveridge and Jie Shan’s paper, “Network of Thrones.”

These researchers calculated how many times each Game of Thrones character appeared within 15 words of another character in A Storm of Swords, the third book in the series.

Network Element	GOT
Node	GOT character
Edge	Mutually mentioned within 15 words
Float	Decimal Numbers
Boolean	True/False

For example, the following sentence counts as an “edge” or connection between Jon Snow and Sam Tarly:

“It was the bastard Jon Snow who had taken that from him, him and his fat friend Sam Tarly.”

“Lucky it might be, and red it certainly was, but Ygritte’s hair was such a tangle that Jon was tempted to ask her if she only brushed it at the changing of the seasons.”

“Arya gave Gendry a sideways look. He said it with me, like Jon used to do, back in Winterfell. She missed Jon Snow the most of all her brothers.””

Pandas Review

Do you need a refresher or introduction to the Python data analysis library Pandas? Be sure to check out Pandas Basics (1-3) in this textbook!

got_df = pd.read_csv('../data/got-edges.csv')

got_df

	Source	Target	Weight
0	Aemon	Grenn	5
1	Aemon	Samwell	31
2	Aerys	Jaime	18
3	Aerys	Robert	6
4	Aerys	Tyrion	5
5	Aerys	Tywin	8
6	Alliser	Mance	5
7	Amory	Oberyn	5
8	Arya	Anguy	11
9	Arya	Beric	23
10	Arya	Bran	9
11	Arya	Brynden	6
12	Arya	Cersei	5
13	Arya	Gendry	43
14	Arya	Gregor	7
15	Arya	Jaime	11
16	Arya	Joffrey	6
17	Arya	Jon	7
18	Arya	Rickon	8
19	Arya	Robert	4
20	Arya	Roose	5
21	Arya	Sandor	46
22	Arya	Thoros	18
23	Arya	Tyrion	5
24	Balon	Loras	4
25	Belwas	Barristan	18
26	Belwas	Illyrio	10
27	Beric	Anguy	4
28	Beric	Gendry	4
29	Beric	Thoros	21
30	Bran	Hodor	96
31	Bran	Jojen	46
32	Bran	Jon	12
33	Bran	Luwin	4
34	Bran	Meera	54
35	Bran	Nan	14
36	Bran	Rickon	35
37	Bran	Samwell	11
38	Bran	Theon	11
39	Brienne	Loras	7
40	Bronn	Gregor	5
41	Bronn	Podrick	19
42	Brynden	Lothar	4
43	Brynden	Walder	5
44	Catelyn	Bran	4
45	Catelyn	Brienne	7
46	Catelyn	Brynden	8
47	Catelyn	Cersei	4
48	Catelyn	Edmure	16
49	Catelyn	Hoster	9
50	Catelyn	Jaime	19
51	Catelyn	Jeyne	10
52	Catelyn	Lysa	8
53	Catelyn	Petyr	5
54	Catelyn	Robb	43
55	Catelyn	Roose	4
56	Catelyn	Roslin	6
57	Catelyn	Sansa	8
58	Catelyn	Stannis	4
59	Catelyn	Tyrion	5
60	Catelyn	Walder	19
61	Cersei	Brienne	5
62	Cersei	Bronn	4
63	Cersei	Elia	4
64	Cersei	Gregor	6
65	Cersei	Ilyn	7
66	Cersei	Jaime	36
67	Cersei	Joffrey	23
68	Cersei	Meryn	10
69	Cersei	Pycelle	4
70	Cersei	Robert	16
71	Cersei	Sandor	6
72	Cersei	Shae	4
73	Cersei	Tyrion	46
74	Cersei	Varys	4
75	Craster	Karl	6
76	Daario	Drogo	4
77	Daario	Irri	5
78	Daenerys	Aegon	8
79	Daenerys	Barristan	20
80	Daenerys	Belwas	26
81	Daenerys	Daario	14
82	Daenerys	Drogo	18
83	Daenerys	Irri	17
84	Daenerys	Jorah	47
85	Daenerys	Kraznys	10
86	Daenerys	Missandei	26
87	Daenerys	Rakharo	7
88	Daenerys	Rhaegar	12
89	Daenerys	Robert	5
90	Daenerys	Viserys	8
91	Daenerys	Worm	14
92	Davos	Cressen	4
93	Davos	Salladhor	16
94	Eddard	Arya	18
95	Eddard	Beric	8
96	Eddard	Bran	15
97	Eddard	Catelyn	5
98	Eddard	Cersei	6
99	Eddard	Jaime	6
100	Eddard	Jon	8
101	Eddard	Rickon	8
102	Eddard	Robb	13
103	Eddard	Robert	10
104	Eddard	Sandor	5
105	Eddard	Sansa	6
106	Eddison	Grenn	8
107	Edmure	Brynden	7
108	Edmure	Lothar	4
109	Edmure	Roslin	16
110	Edmure	Walder	9
111	Gendry	Thoros	7
112	Gilly	Craster	17
113	Gregor	Elia	9
114	Gregor	Ilyn	7
115	Gregor	Meryn	7
116	Gregor	Oberyn	24
117	Gregor	Sandor	12
118	Hodor	Jojen	35
119	Hodor	Meera	41
120	Hoster	Edmure	9
121	Irri	Drogo	7
122	Jaime	Balon	6
123	Jaime	Barristan	4
124	Jaime	Brienne	88
125	Jaime	Edmure	5
126	Jaime	Elia	4
127	Jaime	Gregor	6
128	Jaime	Joffrey	15
129	Jaime	Loras	16
130	Jaime	Meryn	11
131	Jaime	Qyburn	11
132	Jaime	Renly	7
133	Jaime	Robert	17
134	Jaime	Stannis	5
135	Jaime	Tommen	8
136	Jaime	Tyrion	31
137	Janos	Alliser	9
138	Janos	Bowen	5
139	Janos	Mance	4
140	Joffrey	Gregor	5
141	Joffrey	Ilyn	4
142	Joffrey	Kevan	8
143	Joffrey	Loras	7
144	Joffrey	Margaery	28
145	Joffrey	Meryn	5
146	Joffrey	Myrcella	5
147	Joffrey	Oberyn	4
148	Joffrey	Sandor	14
149	Joffrey	Stannis	10
150	Joffrey	Tommen	9
151	Joffrey	Tyrion	54
152	Jojen	Meera	33
153	Jojen	Samwell	11
154	Jon	Aemon	30
155	Jon	Alliser	15
156	Jon	Craster	7
157	Jon	Dalla	6
158	Jon	Eddison	4
159	Jon	Gilly	9
160	Jon	Grenn	25
161	Jon	Janos	8
162	Jon	Mance	69
163	Jon	Meera	4
164	Jon	Melisandre	7
165	Jon	Orell	6
166	Jon	Qhorin	31
167	Jon	Rattleshirt	20
168	Jon	Robert	5
169	Jon	Samwell	52
170	Jon	Stannis	9
171	Jon	Styr	16
172	Jon	Theon	8
173	Jon	Val	12
174	Jon	Ygritte	54
175	Jon Arryn	Lysa	5
176	Jon Arryn	Robert	6
177	Jorah	Barristan	11
178	Jorah	Belwas	13
179	Jorah	Daario	7
180	Jorah	Drogo	6
181	Kevan	Lancel	7
182	Kevan	Varys	5
183	Loras	Margaery	9
184	Loras	Olenna	5
185	Lothar	Roslin	4
186	Luwin	Nan	4
187	Lysa	Cersei	4
188	Lysa	Hoster	6
189	Lysa	Marillion	10
190	Lysa	Petyr	29
191	Lysa	Robert Arryn	9
192	Lysa	Tyrion	5
193	Lysa	Tywin	4
194	Mance	Craster	11
195	Mance	Dalla	8
196	Mance	Gilly	7
197	Mance	Qhorin	10
198	Mance	Rattleshirt	9
199	Mance	Styr	7
200	Mance	Val	8
201	Mance	Ygritte	12
202	Meera	Samwell	7
203	Melisandre	Davos	30
204	Melisandre	Samwell	5
205	Meryn	Ilyn	5
206	Missandei	Irri	4
207	Myrcella	Tommen	5
208	Myrcella	Tyrion	4
209	Oberyn	Ellaria	6
210	Oberyn	Mace	6
211	Podrick	Margaery	4
212	Rattleshirt	Qhorin	6
213	Renly	Loras	8
214	Renly	Margaery	7
215	Renly	Varys	4
216	Rhaegar	Barristan	5
217	Rhaegar	Elia	7
218	Rhaegar	Jorah	5
219	Rhaegar	Robert	6
220	Rickard	Brynden	4
221	Rickon	Theon	8
222	Robb	Arya	15
223	Robb	Balon	6
224	Robb	Bran	23
225	Robb	Brienne	6
226	Robb	Brynden	17
227	Robb	Edmure	32
228	Robb	Hodor	5
229	Robb	Jaime	15
230	Robb	Jeyne	18
231	Robb	Joffrey	10
232	Robb	Jon	14
233	Robb	Lothar	10
234	Robb	Petyr	5
235	Robb	Ramsay	4
236	Robb	Rickard	7
237	Robb	Rickon	15
238	Robb	Roose	4
239	Robb	Sansa	15
240	Robb	Stannis	4
241	Robb	Theon	11
242	Robb	Tyrion	12
243	Robb	Tywin	12
244	Robb	Walder	26
245	Robert	Aemon	4
246	Robert	Barristan	5
247	Robert	Renly	4
248	Robert	Stannis	5
249	Robert	Thoros	4
250	Robert Arryn	Marillion	4
251	Roose	Brienne	4
252	Samwell	Bowen	6
253	Samwell	Craster	34
254	Samwell	Eddison	12
255	Samwell	Gilly	36
256	Samwell	Grenn	43
257	Samwell	Janos	6
258	Samwell	Mance	10
259	Samwell	Qhorin	5
260	Sandor	Beric	15
261	Sandor	Gendry	5
262	Sandor	Ilyn	4
263	Sandor	Meryn	4
264	Sandor	Robert	6
265	Sandor	Thoros	10
266	Sansa	Arya	22
267	Sansa	Bran	10
268	Sansa	Brienne	5
269	Sansa	Cersei	16
270	Sansa	Jaime	10
271	Sansa	Joffrey	35
272	Sansa	Jon	4
273	Sansa	Kevan	5
274	Sansa	Loras	14
275	Sansa	Lysa	28
276	Sansa	Margaery	36
277	Sansa	Marillion	9
278	Sansa	Myrcella	4
279	Sansa	Olenna	7
280	Sansa	Petyr	28
281	Sansa	Podrick	8
282	Sansa	Renly	4
283	Sansa	Rickon	7
284	Sansa	Robert	5
285	Sansa	Robert Arryn	6
286	Sansa	Sandor	6
287	Sansa	Shae	8
288	Sansa	Tyrion	77
289	Shae	Chataya	4
290	Shae	Varys	8
291	Shireen	Davos	5
292	Stannis	Aemon	4
293	Stannis	Balon	4
294	Stannis	Davos	32
295	Stannis	Melisandre	20
296	Stannis	Renly	15
297	Stannis	Samwell	13
298	Tommen	Margaery	5
299	Tyrion	Balon	4
300	Tyrion	Bronn	31
301	Tyrion	Chataya	5
302	Tyrion	Doran	5
303	Tyrion	Elia	5
304	Tyrion	Ellaria	4
305	Tyrion	Gregor	22
306	Tyrion	Ilyn	5
307	Tyrion	Janos	5
308	Tyrion	Kevan	11
309	Tyrion	Loras	6
310	Tyrion	Mace	9
311	Tyrion	Margaery	7
312	Tyrion	Meryn	5
313	Tyrion	Oberyn	25
314	Tyrion	Petyr	12
315	Tyrion	Podrick	28
316	Tyrion	Pycelle	11
317	Tyrion	Renly	6
318	Tyrion	Robert	9
319	Tyrion	Sandor	4
320	Tyrion	Shae	21
321	Tyrion	Stannis	6
322	Tyrion	Varys	18
323	Tywin	Balon	5
324	Tywin	Brynden	4
325	Tywin	Cersei	16
326	Tywin	Gregor	7
327	Tywin	Jaime	13
328	Tywin	Joffrey	13
329	Tywin	Kevan	14
330	Tywin	Mace	5
331	Tywin	Oberyn	6
332	Tywin	Petyr	4
333	Tywin	Podrick	5
334	Tywin	Pycelle	5
335	Tywin	Robert	11
336	Tywin	Stannis	15
337	Tywin	Tommen	4
338	Tywin	Tyrion	39
339	Tywin	Val	4
340	Tywin	Varys	6
341	Tywin	Walder	4
342	Val	Dalla	7
343	Varys	Pycelle	4
344	Viserys	Rhaegar	7
345	Viserys	Tyrion	4
346	Walder	Lothar	12
347	Walder	Petyr	6
348	Walder	Roslin	6
349	Walton	Jaime	10
350	Ygritte	Qhorin	7
351	Ygritte	Rattleshirt	9

Create a Network From a Pandas DataFrame

G = networkx.from_pandas_edgelist(got_df, 'Source', 'Target', 'Weight')

Output a Network File

networkx.write_graphml(G, 'GOT-network.graphml')

Draw a Simple Network

networkx.draw(G)

plt.figure(figsize=(8,8))
networkx.draw(G, with_labels=True, node_color='skyblue', width=.3, font_size=8)

Calculate Degree

Who has the most number of connections in the network?

networkx.degree(G)

DegreeView({'Aemon': 5, 'Grenn': 4, 'Samwell': 15, 'Aerys': 4, 'Jaime': 24, 'Robert': 18, 'Tyrion': 36, 'Tywin': 22, 'Alliser': 3, 'Mance': 12, 'Amory': 1, 'Oberyn': 7, 'Arya': 19, 'Anguy': 2, 'Beric': 6, 'Bran': 14, 'Brynden': 8, 'Cersei': 20, 'Gendry': 4, 'Gregor': 12, 'Joffrey': 18, 'Jon': 26, 'Rickon': 6, 'Roose': 4, 'Sandor': 13, 'Thoros': 5, 'Balon': 6, 'Loras': 9, 'Belwas': 4, 'Barristan': 6, 'Illyrio': 1, 'Hodor': 4, 'Jojen': 4, 'Luwin': 2, 'Meera': 5, 'Nan': 2, 'Theon': 4, 'Brienne': 7, 'Bronn': 4, 'Podrick': 5, 'Lothar': 5, 'Walder': 8, 'Catelyn': 18, 'Edmure': 8, 'Hoster': 3, 'Jeyne': 2, 'Lysa': 10, 'Petyr': 7, 'Robb': 25, 'Roslin': 4, 'Sansa': 26, 'Stannis': 14, 'Elia': 5, 'Ilyn': 6, 'Meryn': 7, 'Pycelle': 4, 'Shae': 5, 'Varys': 7, 'Craster': 5, 'Karl': 1, 'Daario': 4, 'Drogo': 4, 'Irri': 4, 'Daenerys': 14, 'Aegon': 1, 'Jorah': 6, 'Kraznys': 1, 'Missandei': 2, 'Rakharo': 1, 'Rhaegar': 6, 'Viserys': 3, 'Worm': 1, 'Davos': 5, 'Cressen': 1, 'Salladhor': 1, 'Eddard': 12, 'Eddison': 3, 'Gilly': 4, 'Qyburn': 1, 'Renly': 8, 'Tommen': 5, 'Janos': 6, 'Bowen': 2, 'Kevan': 6, 'Margaery': 7, 'Myrcella': 4, 'Dalla': 3, 'Melisandre': 4, 'Orell': 1, 'Qhorin': 5, 'Rattleshirt': 4, 'Styr': 2, 'Val': 4, 'Ygritte': 4, 'Jon Arryn': 2, 'Lancel': 1, 'Olenna': 2, 'Marillion': 3, 'Robert Arryn': 3, 'Ellaria': 2, 'Mace': 3, 'Rickard': 2, 'Ramsay': 1, 'Chataya': 2, 'Shireen': 1, 'Doran': 1, 'Walton': 1})

Make the degree values a dictionary, then add it as a network “attribute” with networkx.set_node_attributes()

degrees = dict(networkx.degree(G))
networkx.set_node_attributes(G, name='degree', values=degrees)

Make a Pandas dataframe from the degree data G.nodes(data='degree'), then sort from highest to lowest

degree_df = pd.DataFrame(G.nodes(data='degree'), columns=['node', 'degree'])
degree_df = degree_df.sort_values(by='degree', ascending=False)
degree_df

	node	degree
6	Tyrion	36
21	Jon	26
50	Sansa	26
48	Robb	25
4	Jaime	24
7	Tywin	22
17	Cersei	20
12	Arya	19
42	Catelyn	18
5	Robert	18
20	Joffrey	18
2	Samwell	15
63	Daenerys	14
15	Bran	14
51	Stannis	14
24	Sandor	13
9	Mance	12
75	Eddard	12
19	Gregor	12
46	Lysa	10
27	Loras	9
16	Brynden	8
41	Walder	8
43	Edmure	8
79	Renly	8
37	Brienne	7
54	Meryn	7
57	Varys	7
47	Petyr	7
84	Margaery	7
11	Oberyn	7
29	Barristan	6
69	Rhaegar	6
83	Kevan	6
81	Janos	6
14	Beric	6
53	Ilyn	6
22	Rickon	6
26	Balon	6
65	Jorah	6
52	Elia	5
56	Shae	5
58	Craster	5
72	Davos	5
89	Qhorin	5
80	Tommen	5
0	Aemon	5
40	Lothar	5
39	Podrick	5
34	Meera	5
25	Thoros	5
77	Gilly	4
62	Irri	4
28	Belwas	4
93	Ygritte	4
90	Rattleshirt	4
23	Roose	4
85	Myrcella	4
31	Hodor	4
18	Gendry	4
32	Jojen	4
60	Daario	4
61	Drogo	4
3	Aerys	4
87	Melisandre	4
55	Pycelle	4
36	Theon	4
1	Grenn	4
38	Bronn	4
49	Roslin	4
92	Val	4
86	Dalla	3
44	Hoster	3
76	Eddison	3
98	Robert Arryn	3
70	Viserys	3
97	Marillion	3
100	Mace	3
8	Alliser	3
91	Styr	2
94	Jon Arryn	2
96	Olenna	2
99	Ellaria	2
101	Rickard	2
103	Chataya	2
45	Jeyne	2
13	Anguy	2
35	Nan	2
33	Luwin	2
82	Bowen	2
67	Missandei	2
10	Amory	1
66	Kraznys	1
105	Doran	1
104	Shireen	1
102	Ramsay	1
59	Karl	1
64	Aegon	1
68	Rakharo	1
95	Lancel	1
30	Illyrio	1
71	Worm	1
73	Cressen	1
74	Salladhor	1
78	Qyburn	1
88	Orell	1
106	Walton	1

Plot the nodes with the highest degree values

num_nodes_to_inspect = 10
degree_df[:num_nodes_to_inspect].plot(x='node', y='degree', kind='barh').invert_yaxis()

Calculate Weighted Degree

Who has the most number of connections in the network (if you factor in edge weight)?

networkx.degree(G, weight='Weight')

DegreeView({'Aemon': 74, 'Grenn': 81, 'Samwell': 282, 'Aerys': 37, 'Jaime': 372, 'Robert': 128, 'Tyrion': 551, 'Tywin': 204, 'Alliser': 29, 'Mance': 160, 'Amory': 5, 'Oberyn': 76, 'Arya': 269, 'Anguy': 15, 'Beric': 75, 'Bran': 344, 'Brynden': 55, 'Cersei': 226, 'Gendry': 59, 'Gregor': 117, 'Joffrey': 255, 'Jon': 442, 'Rickon': 81, 'Roose': 17, 'Sandor': 137, 'Thoros': 60, 'Balon': 29, 'Loras': 76, 'Belwas': 67, 'Barristan': 63, 'Illyrio': 10, 'Hodor': 177, 'Jojen': 125, 'Luwin': 8, 'Meera': 139, 'Nan': 18, 'Theon': 38, 'Brienne': 122, 'Bronn': 59, 'Podrick': 64, 'Lothar': 34, 'Walder': 87, 'Catelyn': 184, 'Edmure': 98, 'Hoster': 24, 'Jeyne': 28, 'Lysa': 108, 'Petyr': 89, 'Robb': 342, 'Roslin': 32, 'Sansa': 383, 'Stannis': 146, 'Elia': 29, 'Ilyn': 32, 'Meryn': 47, 'Pycelle': 24, 'Shae': 45, 'Varys': 49, 'Craster': 75, 'Karl': 6, 'Daario': 30, 'Drogo': 35, 'Irri': 33, 'Daenerys': 232, 'Aegon': 8, 'Jorah': 89, 'Kraznys': 10, 'Missandei': 30, 'Rakharo': 7, 'Rhaegar': 42, 'Viserys': 19, 'Worm': 14, 'Davos': 87, 'Cressen': 4, 'Salladhor': 16, 'Eddard': 108, 'Eddison': 24, 'Gilly': 69, 'Qyburn': 11, 'Renly': 55, 'Tommen': 31, 'Janos': 37, 'Bowen': 11, 'Kevan': 50, 'Margaery': 96, 'Myrcella': 18, 'Dalla': 21, 'Melisandre': 62, 'Orell': 6, 'Qhorin': 59, 'Rattleshirt': 44, 'Styr': 23, 'Val': 31, 'Ygritte': 82, 'Jon Arryn': 11, 'Lancel': 7, 'Olenna': 12, 'Marillion': 23, 'Robert Arryn': 19, 'Ellaria': 10, 'Mace': 20, 'Rickard': 11, 'Ramsay': 4, 'Chataya': 9, 'Shireen': 5, 'Doran': 5, 'Walton': 10})

Make the weighted degree values a dictionary, then add it as a network “attribute” with networkx.set_node_attributes()

weighted_degrees = dict(networkx.degree(G, weight='Weight'))
networkx.set_node_attributes(G, name='weighted_degree', values=weighted_degrees)

Make a Pandas dataframe from the degree data G.nodes(data='weighted_degree'), then sort from highest to lowest

weighted_degree_df = pd.DataFrame(G.nodes(data='weighted_degree'), columns=['node', 'weighted_degree'])
weighted_degree_df = weighted_degree_df.sort_values(by='weighted_degree', ascending=False)
weighted_degree_df

	node	weighted_degree
6	Tyrion	551
21	Jon	442
50	Sansa	383
4	Jaime	372
15	Bran	344
48	Robb	342
2	Samwell	282
12	Arya	269
20	Joffrey	255
63	Daenerys	232
17	Cersei	226
7	Tywin	204
42	Catelyn	184
31	Hodor	177
9	Mance	160
51	Stannis	146
34	Meera	139
24	Sandor	137
5	Robert	128
32	Jojen	125
37	Brienne	122
19	Gregor	117
46	Lysa	108
75	Eddard	108
43	Edmure	98
84	Margaery	96
47	Petyr	89
65	Jorah	89
72	Davos	87
41	Walder	87
93	Ygritte	82
1	Grenn	81
22	Rickon	81
11	Oberyn	76
27	Loras	76
14	Beric	75
58	Craster	75
0	Aemon	74
77	Gilly	69
28	Belwas	67
39	Podrick	64
29	Barristan	63
87	Melisandre	62
25	Thoros	60
38	Bronn	59
89	Qhorin	59
18	Gendry	59
79	Renly	55
16	Brynden	55
83	Kevan	50
57	Varys	49
54	Meryn	47
56	Shae	45
90	Rattleshirt	44
69	Rhaegar	42
36	Theon	38
81	Janos	37
3	Aerys	37
61	Drogo	35
40	Lothar	34
62	Irri	33
53	Ilyn	32
49	Roslin	32
92	Val	31
80	Tommen	31
60	Daario	30
67	Missandei	30
52	Elia	29
8	Alliser	29
26	Balon	29
45	Jeyne	28
55	Pycelle	24
76	Eddison	24
44	Hoster	24
91	Styr	23
97	Marillion	23
86	Dalla	21
100	Mace	20
70	Viserys	19
98	Robert Arryn	19
35	Nan	18
85	Myrcella	18
23	Roose	17
74	Salladhor	16
13	Anguy	15
71	Worm	14
96	Olenna	12
78	Qyburn	11
82	Bowen	11
101	Rickard	11
94	Jon Arryn	11
99	Ellaria	10
106	Walton	10
30	Illyrio	10
66	Kraznys	10
103	Chataya	9
64	Aegon	8
33	Luwin	8
95	Lancel	7
68	Rakharo	7
59	Karl	6
88	Orell	6
10	Amory	5
104	Shireen	5
105	Doran	5
73	Cressen	4
102	Ramsay	4

Plot the nodes with the highest weighted degree values

num_nodes_to_inspect = 10
weighted_degree_df[:num_nodes_to_inspect].plot(x='node', y='weighted_degree', color='orange', kind='barh').invert_yaxis()

Calculate Betweenness Centrality Scores

Who connects the most other nodes in the network?

networkx.betweenness_centrality(G)

{'Aemon': 0.004622508177397137,
 'Grenn': 5.989817310572027e-05,
 'Samwell': 0.03227262859511145,
 'Aerys': 0.0,
 'Jaime': 0.09994344156136246,
 'Robert': 0.2094523840293194,
 'Tyrion': 0.19791284320277353,
 'Tywin': 0.06553840423231058,
 'Alliser': 0.0,
 'Mance': 0.007275961371756524,
 'Amory': 0.0,
 'Oberyn': 0.01958014232946847,
 'Arya': 0.07960711308183888,
 'Anguy': 0.0,
 'Beric': 0.0006091214266627511,
 'Bran': 0.06301659941786954,
 'Brynden': 0.004727134574764742,
 'Cersei': 0.02674643287824232,
 'Gendry': 0.0,
 'Gregor': 0.005879915417737236,
 'Joffrey': 0.025434099443917696,
 'Jon': 0.22996466368473173,
 'Rickon': 0.0007626320968639029,
 'Roose': 0.0002629599012559427,
 'Sandor': 0.014162278987154626,
 'Thoros': 0.0019261823273239152,
 'Balon': 0.0006281164805527878,
 'Loras': 0.003780752291533962,
 'Belwas': 0.018867924528301886,
 'Barristan': 0.04011407489067884,
 'Illyrio': 0.0,
 'Hodor': 0.0021937273446681065,
 'Jojen': 0.0004064518889316733,
 'Luwin': 0.0,
 'Meera': 0.0019922569692430686,
 'Nan': 0.0,
 'Theon': 0.00047177590304274953,
 'Brienne': 0.0015376001520469229,
 'Bronn': 0.00010482180293501046,
 'Podrick': 0.0008358985235471674,
 'Lothar': 0.0006600525898733143,
 'Walder': 0.0032731813973401044,
 'Catelyn': 0.04890588265764755,
 'Edmure': 0.0063172332740347534,
 'Hoster': 0.0002426336765959408,
 'Jeyne': 0.0,
 'Lysa': 0.015215156305177053,
 'Petyr': 0.0011502139845250673,
 'Robb': 0.1269644713830151,
 'Roslin': 6.751960581039384e-05,
 'Sansa': 0.12672031668498004,
 'Stannis': 0.1026998617273264,
 'Elia': 0.002996853410630337,
 'Ilyn': 0.0,
 'Meryn': 0.00025900705642512993,
 'Pycelle': 0.0,
 'Shae': 0.0012523948930099663,
 'Varys': 0.0009094137867722772,
 'Craster': 0.018867924528301886,
 'Karl': 0.0,
 'Daario': 5.989817310572027e-05,
 'Drogo': 5.989817310572027e-05,
 'Irri': 0.00017969451931716083,
 'Daenerys': 0.15720345212054956,
 'Aegon': 0.0,
 'Jorah': 0.002329891127259132,
 'Kraznys': 0.0,
 'Missandei': 0.0,
 'Rakharo': 0.0,
 'Rhaegar': 0.007312671807481101,
 'Viserys': 0.015644391307527945,
 'Worm': 0.0,
 'Davos': 0.056064690026954175,
 'Cressen': 0.0,
 'Salladhor': 0.0,
 'Eddard': 0.019341416465723616,
 'Eddison': 0.0,
 'Gilly': 0.0,
 'Qyburn': 0.0,
 'Renly': 0.004227926858198188,
 'Tommen': 0.0006984420191967362,
 'Janos': 0.02355861153253357,
 'Bowen': 0.0,
 'Kevan': 0.019219915043818533,
 'Margaery': 0.0010973522354777933,
 'Myrcella': 0.0006235971795124951,
 'Dalla': 0.0,
 'Melisandre': 0.00860866854841803,
 'Orell': 0.0,
 'Qhorin': 0.0002635519616651692,
 'Rattleshirt': 0.0,
 'Styr': 0.0,
 'Val': 0.005898334987101542,
 'Ygritte': 0.0,
 'Jon Arryn': 0.0005764583325230229,
 'Lancel': 0.0,
 'Olenna': 0.0,
 'Marillion': 0.0,
 'Robert Arryn': 0.0,
 'Ellaria': 0.0,
 'Mace': 0.0,
 'Rickard': 0.0,
 'Ramsay': 0.0,
 'Chataya': 0.0,
 'Shireen': 0.0,
 'Doran': 0.0,
 'Walton': 0.0}

betweenness_centrality = networkx.betweenness_centrality(G)

Add betweenness_centrality (which is already a dictionary) as a network “attribute” with networkx.set_node_attributes()

networkx.set_node_attributes(G, name='betweenness', values=betweenness_centrality)

Make a Pandas dataframe from the betweenness data G.nodes(data='betweenness'), then sort from highest to lowest

betweenness_df = pd.DataFrame(G.nodes(data='betweenness'), columns=['node', 'betweenness'])
betweenness_df = betweenness_df.sort_values(by='betweenness', ascending=False)
betweenness_df

	node	betweenness
21	Jon	0.229965
5	Robert	0.209452
6	Tyrion	0.197913
63	Daenerys	0.157203
48	Robb	0.126964
50	Sansa	0.126720
51	Stannis	0.102700
4	Jaime	0.099943
12	Arya	0.079607
7	Tywin	0.065538
15	Bran	0.063017
72	Davos	0.056065
42	Catelyn	0.048906
29	Barristan	0.040114
2	Samwell	0.032273
17	Cersei	0.026746
20	Joffrey	0.025434
81	Janos	0.023559
11	Oberyn	0.019580
75	Eddard	0.019341
83	Kevan	0.019220
28	Belwas	0.018868
58	Craster	0.018868
70	Viserys	0.015644
46	Lysa	0.015215
24	Sandor	0.014162
87	Melisandre	0.008609
69	Rhaegar	0.007313
9	Mance	0.007276
43	Edmure	0.006317
92	Val	0.005898
19	Gregor	0.005880
16	Brynden	0.004727
0	Aemon	0.004623
79	Renly	0.004228
27	Loras	0.003781
41	Walder	0.003273
52	Elia	0.002997
65	Jorah	0.002330
31	Hodor	0.002194
34	Meera	0.001992
25	Thoros	0.001926
37	Brienne	0.001538
56	Shae	0.001252
47	Petyr	0.001150
84	Margaery	0.001097
57	Varys	0.000909
39	Podrick	0.000836
22	Rickon	0.000763
80	Tommen	0.000698
40	Lothar	0.000660
26	Balon	0.000628
85	Myrcella	0.000624
14	Beric	0.000609
94	Jon Arryn	0.000576
36	Theon	0.000472
32	Jojen	0.000406
89	Qhorin	0.000264
23	Roose	0.000263
54	Meryn	0.000259
44	Hoster	0.000243
62	Irri	0.000180
38	Bronn	0.000105
49	Roslin	0.000068
60	Daario	0.000060
61	Drogo	0.000060
1	Grenn	0.000060
97	Marillion	0.000000
102	Ramsay	0.000000
105	Doran	0.000000
100	Mace	0.000000
104	Shireen	0.000000
98	Robert Arryn	0.000000
86	Dalla	0.000000
103	Chataya	0.000000
88	Orell	0.000000
93	Ygritte	0.000000
96	Olenna	0.000000
90	Rattleshirt	0.000000
101	Rickard	0.000000
95	Lancel	0.000000
91	Styr	0.000000
99	Ellaria	0.000000
53	Ilyn	0.000000
82	Bowen	0.000000
55	Pycelle	0.000000
3	Aerys	0.000000
8	Alliser	0.000000
10	Amory	0.000000
13	Anguy	0.000000
18	Gendry	0.000000
30	Illyrio	0.000000
33	Luwin	0.000000
35	Nan	0.000000
45	Jeyne	0.000000
59	Karl	0.000000
78	Qyburn	0.000000
64	Aegon	0.000000
66	Kraznys	0.000000
67	Missandei	0.000000
68	Rakharo	0.000000
71	Worm	0.000000
73	Cressen	0.000000
74	Salladhor	0.000000
76	Eddison	0.000000
77	Gilly	0.000000
106	Walton	0.000000

Plot the nodes with the highest betweenness centrality scores

num_nodes_to_inspect = 10
betweenness_df[:num_nodes_to_inspect].plot(x='node', y='betweenness', color='green', kind='barh').invert_yaxis()

Communities

Who forms distinct communities within this network?

from networkx.algorithms import community

Calculate communities with community.greedy_modularity_communities()

communities = community.greedy_modularity_communities(G)

communities

Show code cell output Hide code cell output

[frozenset({'Aerys',
            'Amory',
            'Balon',
            'Bronn',
            'Chataya',
            'Doran',
            'Elia',
            'Ellaria',
            'Gregor',
            'Ilyn',
            'Jaime',
            'Joffrey',
            'Kevan',
            'Lancel',
            'Loras',
            'Mace',
            'Margaery',
            'Meryn',
            'Myrcella',
            'Oberyn',
            'Olenna',
            'Podrick',
            'Pycelle',
            'Qyburn',
            'Renly',
            'Sandor',
            'Shae',
            'Tommen',
            'Tyrion',
            'Tywin',
            'Varys',
            'Walton'}),
 frozenset({'Aemon',
            'Alliser',
            'Bowen',
            'Craster',
            'Cressen',
            'Dalla',
            'Davos',
            'Eddison',
            'Gilly',
            'Grenn',
            'Hodor',
            'Janos',
            'Jojen',
            'Jon',
            'Karl',
            'Mance',
            'Meera',
            'Melisandre',
            'Orell',
            'Qhorin',
            'Rattleshirt',
            'Salladhor',
            'Samwell',
            'Shireen',
            'Stannis',
            'Styr',
            'Val',
            'Ygritte'}),
 frozenset({'Arya',
            'Bran',
            'Brienne',
            'Brynden',
            'Catelyn',
            'Cersei',
            'Eddard',
            'Edmure',
            'Hoster',
            'Jeyne',
            'Lothar',
            'Luwin',
            'Lysa',
            'Marillion',
            'Nan',
            'Petyr',
            'Ramsay',
            'Rickard',
            'Rickon',
            'Robb',
            'Robert Arryn',
            'Roose',
            'Roslin',
            'Sansa',
            'Theon',
            'Walder'}),
 frozenset({'Aegon',
            'Barristan',
            'Belwas',
            'Daario',
            'Daenerys',
            'Drogo',
            'Illyrio',
            'Irri',
            'Jon Arryn',
            'Jorah',
            'Kraznys',
            'Missandei',
            'Rakharo',
            'Rhaegar',
            'Robert',
            'Viserys',
            'Worm'}),
 frozenset({'Anguy', 'Beric', 'Gendry', 'Thoros'})]

Make a dictionary by looping through the communities and, for each member of the community, adding their community number

# Create empty dictionary
modularity_class = {}
#Loop through each community in the network
for community_number, community in enumerate(communities):
    #For each member of the community, add their community number
    for name in community:
        modularity_class[name] = community_number

Add modularity class to the network as an attribute

networkx.set_node_attributes(G, modularity_class, 'modularity_class')

Make a Pandas dataframe from modularity class network data G.nodes(data='modularity_class')

communities_df = pd.DataFrame(G.nodes(data='modularity_class'), columns=['node', 'modularity_class'])
communities_df = communities_df.sort_values(by='modularity_class', ascending=False)

communities_df

	node	modularity_class
25	Thoros	4
18	Gendry	4
14	Beric	4
13	Anguy	4
28	Belwas	3
94	Jon Arryn	3
65	Jorah	3
67	Missandei	3
71	Worm	3
70	Viserys	3
29	Barristan	3
30	Illyrio	3
69	Rhaegar	3
68	Rakharo	3
64	Aegon	3
63	Daenerys	3
5	Robert	3
62	Irri	3
61	Drogo	3
60	Daario	3
66	Kraznys	3
36	Theon	2
37	Brienne	2
43	Edmure	2
40	Lothar	2
42	Catelyn	2
44	Hoster	2
45	Jeyne	2
46	Lysa	2
47	Petyr	2
48	Robb	2
49	Roslin	2
50	Sansa	2
35	Nan	2
41	Walder	2
33	Luwin	2
17	Cersei	2
102	Ramsay	2
75	Eddard	2
101	Rickard	2
15	Bran	2
16	Brynden	2
12	Arya	2
98	Robert Arryn	2
22	Rickon	2
23	Roose	2
97	Marillion	2
73	Cressen	1
72	Davos	1
58	Craster	1
74	Salladhor	1
76	Eddison	1
77	Gilly	1
87	Melisandre	1
86	Dalla	1
81	Janos	1
59	Karl	1
0	Aemon	1
88	Orell	1
32	Jojen	1
2	Samwell	1
104	Shireen	1
8	Alliser	1
9	Mance	1
21	Jon	1
89	Qhorin	1
31	Hodor	1
82	Bowen	1
34	Meera	1
93	Ygritte	1
51	Stannis	1
92	Val	1
1	Grenn	1
91	Styr	1
90	Rattleshirt	1
84	Margaery	0
96	Olenna	0
99	Ellaria	0
100	Mace	0
103	Chataya	0
105	Doran	0
85	Myrcella	0
95	Lancel	0
53	Ilyn	0
83	Kevan	0
26	Balon	0
3	Aerys	0
4	Jaime	0
6	Tyrion	0
7	Tywin	0
10	Amory	0
11	Oberyn	0
19	Gregor	0
20	Joffrey	0
24	Sandor	0
27	Loras	0
80	Tommen	0
38	Bronn	0
39	Podrick	0
52	Elia	0
54	Meryn	0
55	Pycelle	0
56	Shae	0
57	Varys	0
78	Qyburn	0
79	Renly	0
106	Walton	0

Inspect each community in the network

communities_df[communities_df['modularity_class'] == 4]

	node	modularity_class
25	Thoros	4
18	Gendry	4
14	Beric	4
13	Anguy	4

communities_df[communities_df['modularity_class'] == 3]

	node	modularity_class
28	Belwas	3
94	Jon Arryn	3
65	Jorah	3
67	Missandei	3
71	Worm	3
70	Viserys	3
29	Barristan	3
30	Illyrio	3
69	Rhaegar	3
68	Rakharo	3
64	Aegon	3
63	Daenerys	3
5	Robert	3
62	Irri	3
61	Drogo	3
60	Daario	3
66	Kraznys	3

communities_df[communities_df['modularity_class'] == 2]

	node	modularity_class
36	Theon	2
37	Brienne	2
43	Edmure	2
40	Lothar	2
42	Catelyn	2
44	Hoster	2
45	Jeyne	2
46	Lysa	2
47	Petyr	2
48	Robb	2
49	Roslin	2
50	Sansa	2
35	Nan	2
41	Walder	2
33	Luwin	2
17	Cersei	2
102	Ramsay	2
75	Eddard	2
101	Rickard	2
15	Bran	2
16	Brynden	2
12	Arya	2
98	Robert Arryn	2
22	Rickon	2
23	Roose	2
97	Marillion	2

communities_df[communities_df['modularity_class'] == 1]

	node	modularity_class
73	Cressen	1
72	Davos	1
58	Craster	1
74	Salladhor	1
76	Eddison	1
77	Gilly	1
87	Melisandre	1
86	Dalla	1
81	Janos	1
59	Karl	1
0	Aemon	1
88	Orell	1
32	Jojen	1
2	Samwell	1
104	Shireen	1
8	Alliser	1
9	Mance	1
21	Jon	1
89	Qhorin	1
31	Hodor	1
82	Bowen	1
34	Meera	1
93	Ygritte	1
51	Stannis	1
92	Val	1
1	Grenn	1
91	Styr	1
90	Rattleshirt	1

communities_df[communities_df['modularity_class'] == 0]

	node	modularity_class
84	Margaery	0
96	Olenna	0
99	Ellaria	0
100	Mace	0
103	Chataya	0
105	Doran	0
85	Myrcella	0
95	Lancel	0
53	Ilyn	0
83	Kevan	0
26	Balon	0
3	Aerys	0
4	Jaime	0
6	Tyrion	0
7	Tywin	0
10	Amory	0
11	Oberyn	0
19	Gregor	0
20	Joffrey	0
24	Sandor	0
27	Loras	0
80	Tommen	0
38	Bronn	0
39	Podrick	0
52	Elia	0
54	Meryn	0
55	Pycelle	0
56	Shae	0
57	Varys	0
78	Qyburn	0
79	Renly	0
106	Walton	0

Plot a sample of 40 characters with their modularity class indicated by a star

import seaborn as sns

#Set figure size
plt.figure(figsize=(4,12))

#Plot a categorical scatter plot from the dataframe communities_df.sample(40)
ax =sns.stripplot(x='modularity_class', y='node', data=communities_df.sample(40),
              hue='modularity_class', marker='*',size=15)
#Set legend outside the plot with bbox_to_anchor
ax.legend(loc='upper right',bbox_to_anchor=(1.5, 1), title='Modularity Class')
ax.set_title("GOT Characters By Modularity Class\n(Random Sample)")
plt.show()

Plot all GOT characters with their modularity class indicated by a star (tak

plt.figure(figsize=(4,25))

ax =sns.stripplot(x='modularity_class', y='node', data=communities_df,
              hue='modularity_class', marker='*',size=15)

ax.legend(loc='upper right',bbox_to_anchor=(1.5, 1), title='Modularity Class')
ax.set_title("GOT Characters By Modularity Class")
plt.show()

All Network Metrics

Create a Pandas dataframe of all network attributes by creating a dictionary of G.nodes(data=True)…

dict(G.nodes(data=True))

{'Aemon': {'degree': 5,
  'weighted_degree': 74,
  'betweenness': 0.004622508177397137,
  'modularity_class': 1},
 'Grenn': {'degree': 4,
  'weighted_degree': 81,
  'betweenness': 5.989817310572027e-05,
  'modularity_class': 1},
 'Samwell': {'degree': 15,
  'weighted_degree': 282,
  'betweenness': 0.03227262859511145,
  'modularity_class': 1},
 'Aerys': {'degree': 4,
  'weighted_degree': 37,
  'betweenness': 0.0,
  'modularity_class': 0},
 'Jaime': {'degree': 24,
  'weighted_degree': 372,
  'betweenness': 0.09994344156136246,
  'modularity_class': 0},
 'Robert': {'degree': 18,
  'weighted_degree': 128,
  'betweenness': 0.2094523840293194,
  'modularity_class': 3},
 'Tyrion': {'degree': 36,
  'weighted_degree': 551,
  'betweenness': 0.19791284320277353,
  'modularity_class': 0},
 'Tywin': {'degree': 22,
  'weighted_degree': 204,
  'betweenness': 0.06553840423231058,
  'modularity_class': 0},
 'Alliser': {'degree': 3,
  'weighted_degree': 29,
  'betweenness': 0.0,
  'modularity_class': 1},
 'Mance': {'degree': 12,
  'weighted_degree': 160,
  'betweenness': 0.007275961371756524,
  'modularity_class': 1},
 'Amory': {'degree': 1,
  'weighted_degree': 5,
  'betweenness': 0.0,
  'modularity_class': 0},
 'Oberyn': {'degree': 7,
  'weighted_degree': 76,
  'betweenness': 0.01958014232946847,
  'modularity_class': 0},
 'Arya': {'degree': 19,
  'weighted_degree': 269,
  'betweenness': 0.07960711308183888,
  'modularity_class': 2},
 'Anguy': {'degree': 2,
  'weighted_degree': 15,
  'betweenness': 0.0,
  'modularity_class': 4},
 'Beric': {'degree': 6,
  'weighted_degree': 75,
  'betweenness': 0.0006091214266627511,
  'modularity_class': 4},
 'Bran': {'degree': 14,
  'weighted_degree': 344,
  'betweenness': 0.06301659941786954,
  'modularity_class': 2},
 'Brynden': {'degree': 8,
  'weighted_degree': 55,
  'betweenness': 0.004727134574764742,
  'modularity_class': 2},
 'Cersei': {'degree': 20,
  'weighted_degree': 226,
  'betweenness': 0.02674643287824232,
  'modularity_class': 2},
 'Gendry': {'degree': 4,
  'weighted_degree': 59,
  'betweenness': 0.0,
  'modularity_class': 4},
 'Gregor': {'degree': 12,
  'weighted_degree': 117,
  'betweenness': 0.005879915417737236,
  'modularity_class': 0},
 'Joffrey': {'degree': 18,
  'weighted_degree': 255,
  'betweenness': 0.025434099443917696,
  'modularity_class': 0},
 'Jon': {'degree': 26,
  'weighted_degree': 442,
  'betweenness': 0.22996466368473173,
  'modularity_class': 1},
 'Rickon': {'degree': 6,
  'weighted_degree': 81,
  'betweenness': 0.0007626320968639029,
  'modularity_class': 2},
 'Roose': {'degree': 4,
  'weighted_degree': 17,
  'betweenness': 0.0002629599012559427,
  'modularity_class': 2},
 'Sandor': {'degree': 13,
  'weighted_degree': 137,
  'betweenness': 0.014162278987154626,
  'modularity_class': 0},
 'Thoros': {'degree': 5,
  'weighted_degree': 60,
  'betweenness': 0.0019261823273239152,
  'modularity_class': 4},
 'Balon': {'degree': 6,
  'weighted_degree': 29,
  'betweenness': 0.0006281164805527878,
  'modularity_class': 0},
 'Loras': {'degree': 9,
  'weighted_degree': 76,
  'betweenness': 0.003780752291533962,
  'modularity_class': 0},
 'Belwas': {'degree': 4,
  'weighted_degree': 67,
  'betweenness': 0.018867924528301886,
  'modularity_class': 3},
 'Barristan': {'degree': 6,
  'weighted_degree': 63,
  'betweenness': 0.04011407489067884,
  'modularity_class': 3},
 'Illyrio': {'degree': 1,
  'weighted_degree': 10,
  'betweenness': 0.0,
  'modularity_class': 3},
 'Hodor': {'degree': 4,
  'weighted_degree': 177,
  'betweenness': 0.0021937273446681065,
  'modularity_class': 1},
 'Jojen': {'degree': 4,
  'weighted_degree': 125,
  'betweenness': 0.0004064518889316733,
  'modularity_class': 1},
 'Luwin': {'degree': 2,
  'weighted_degree': 8,
  'betweenness': 0.0,
  'modularity_class': 2},
 'Meera': {'degree': 5,
  'weighted_degree': 139,
  'betweenness': 0.0019922569692430686,
  'modularity_class': 1},
 'Nan': {'degree': 2,
  'weighted_degree': 18,
  'betweenness': 0.0,
  'modularity_class': 2},
 'Theon': {'degree': 4,
  'weighted_degree': 38,
  'betweenness': 0.00047177590304274953,
  'modularity_class': 2},
 'Brienne': {'degree': 7,
  'weighted_degree': 122,
  'betweenness': 0.0015376001520469229,
  'modularity_class': 2},
 'Bronn': {'degree': 4,
  'weighted_degree': 59,
  'betweenness': 0.00010482180293501046,
  'modularity_class': 0},
 'Podrick': {'degree': 5,
  'weighted_degree': 64,
  'betweenness': 0.0008358985235471674,
  'modularity_class': 0},
 'Lothar': {'degree': 5,
  'weighted_degree': 34,
  'betweenness': 0.0006600525898733143,
  'modularity_class': 2},
 'Walder': {'degree': 8,
  'weighted_degree': 87,
  'betweenness': 0.0032731813973401044,
  'modularity_class': 2},
 'Catelyn': {'degree': 18,
  'weighted_degree': 184,
  'betweenness': 0.04890588265764755,
  'modularity_class': 2},
 'Edmure': {'degree': 8,
  'weighted_degree': 98,
  'betweenness': 0.0063172332740347534,
  'modularity_class': 2},
 'Hoster': {'degree': 3,
  'weighted_degree': 24,
  'betweenness': 0.0002426336765959408,
  'modularity_class': 2},
 'Jeyne': {'degree': 2,
  'weighted_degree': 28,
  'betweenness': 0.0,
  'modularity_class': 2},
 'Lysa': {'degree': 10,
  'weighted_degree': 108,
  'betweenness': 0.015215156305177053,
  'modularity_class': 2},
 'Petyr': {'degree': 7,
  'weighted_degree': 89,
  'betweenness': 0.0011502139845250673,
  'modularity_class': 2},
 'Robb': {'degree': 25,
  'weighted_degree': 342,
  'betweenness': 0.1269644713830151,
  'modularity_class': 2},
 'Roslin': {'degree': 4,
  'weighted_degree': 32,
  'betweenness': 6.751960581039384e-05,
  'modularity_class': 2},
 'Sansa': {'degree': 26,
  'weighted_degree': 383,
  'betweenness': 0.12672031668498004,
  'modularity_class': 2},
 'Stannis': {'degree': 14,
  'weighted_degree': 146,
  'betweenness': 0.1026998617273264,
  'modularity_class': 1},
 'Elia': {'degree': 5,
  'weighted_degree': 29,
  'betweenness': 0.002996853410630337,
  'modularity_class': 0},
 'Ilyn': {'degree': 6,
  'weighted_degree': 32,
  'betweenness': 0.0,
  'modularity_class': 0},
 'Meryn': {'degree': 7,
  'weighted_degree': 47,
  'betweenness': 0.00025900705642512993,
  'modularity_class': 0},
 'Pycelle': {'degree': 4,
  'weighted_degree': 24,
  'betweenness': 0.0,
  'modularity_class': 0},
 'Shae': {'degree': 5,
  'weighted_degree': 45,
  'betweenness': 0.0012523948930099663,
  'modularity_class': 0},
 'Varys': {'degree': 7,
  'weighted_degree': 49,
  'betweenness': 0.0009094137867722772,
  'modularity_class': 0},
 'Craster': {'degree': 5,
  'weighted_degree': 75,
  'betweenness': 0.018867924528301886,
  'modularity_class': 1},
 'Karl': {'degree': 1,
  'weighted_degree': 6,
  'betweenness': 0.0,
  'modularity_class': 1},
 'Daario': {'degree': 4,
  'weighted_degree': 30,
  'betweenness': 5.989817310572027e-05,
  'modularity_class': 3},
 'Drogo': {'degree': 4,
  'weighted_degree': 35,
  'betweenness': 5.989817310572027e-05,
  'modularity_class': 3},
 'Irri': {'degree': 4,
  'weighted_degree': 33,
  'betweenness': 0.00017969451931716083,
  'modularity_class': 3},
 'Daenerys': {'degree': 14,
  'weighted_degree': 232,
  'betweenness': 0.15720345212054956,
  'modularity_class': 3},
 'Aegon': {'degree': 1,
  'weighted_degree': 8,
  'betweenness': 0.0,
  'modularity_class': 3},
 'Jorah': {'degree': 6,
  'weighted_degree': 89,
  'betweenness': 0.002329891127259132,
  'modularity_class': 3},
 'Kraznys': {'degree': 1,
  'weighted_degree': 10,
  'betweenness': 0.0,
  'modularity_class': 3},
 'Missandei': {'degree': 2,
  'weighted_degree': 30,
  'betweenness': 0.0,
  'modularity_class': 3},
 'Rakharo': {'degree': 1,
  'weighted_degree': 7,
  'betweenness': 0.0,
  'modularity_class': 3},
 'Rhaegar': {'degree': 6,
  'weighted_degree': 42,
  'betweenness': 0.007312671807481101,
  'modularity_class': 3},
 'Viserys': {'degree': 3,
  'weighted_degree': 19,
  'betweenness': 0.015644391307527945,
  'modularity_class': 3},
 'Worm': {'degree': 1,
  'weighted_degree': 14,
  'betweenness': 0.0,
  'modularity_class': 3},
 'Davos': {'degree': 5,
  'weighted_degree': 87,
  'betweenness': 0.056064690026954175,
  'modularity_class': 1},
 'Cressen': {'degree': 1,
  'weighted_degree': 4,
  'betweenness': 0.0,
  'modularity_class': 1},
 'Salladhor': {'degree': 1,
  'weighted_degree': 16,
  'betweenness': 0.0,
  'modularity_class': 1},
 'Eddard': {'degree': 12,
  'weighted_degree': 108,
  'betweenness': 0.019341416465723616,
  'modularity_class': 2},
 'Eddison': {'degree': 3,
  'weighted_degree': 24,
  'betweenness': 0.0,
  'modularity_class': 1},
 'Gilly': {'degree': 4,
  'weighted_degree': 69,
  'betweenness': 0.0,
  'modularity_class': 1},
 'Qyburn': {'degree': 1,
  'weighted_degree': 11,
  'betweenness': 0.0,
  'modularity_class': 0},
 'Renly': {'degree': 8,
  'weighted_degree': 55,
  'betweenness': 0.004227926858198188,
  'modularity_class': 0},
 'Tommen': {'degree': 5,
  'weighted_degree': 31,
  'betweenness': 0.0006984420191967362,
  'modularity_class': 0},
 'Janos': {'degree': 6,
  'weighted_degree': 37,
  'betweenness': 0.02355861153253357,
  'modularity_class': 1},
 'Bowen': {'degree': 2,
  'weighted_degree': 11,
  'betweenness': 0.0,
  'modularity_class': 1},
 'Kevan': {'degree': 6,
  'weighted_degree': 50,
  'betweenness': 0.019219915043818533,
  'modularity_class': 0},
 'Margaery': {'degree': 7,
  'weighted_degree': 96,
  'betweenness': 0.0010973522354777933,
  'modularity_class': 0},
 'Myrcella': {'degree': 4,
  'weighted_degree': 18,
  'betweenness': 0.0006235971795124951,
  'modularity_class': 0},
 'Dalla': {'degree': 3,
  'weighted_degree': 21,
  'betweenness': 0.0,
  'modularity_class': 1},
 'Melisandre': {'degree': 4,
  'weighted_degree': 62,
  'betweenness': 0.00860866854841803,
  'modularity_class': 1},
 'Orell': {'degree': 1,
  'weighted_degree': 6,
  'betweenness': 0.0,
  'modularity_class': 1},
 'Qhorin': {'degree': 5,
  'weighted_degree': 59,
  'betweenness': 0.0002635519616651692,
  'modularity_class': 1},
 'Rattleshirt': {'degree': 4,
  'weighted_degree': 44,
  'betweenness': 0.0,
  'modularity_class': 1},
 'Styr': {'degree': 2,
  'weighted_degree': 23,
  'betweenness': 0.0,
  'modularity_class': 1},
 'Val': {'degree': 4,
  'weighted_degree': 31,
  'betweenness': 0.005898334987101542,
  'modularity_class': 1},
 'Ygritte': {'degree': 4,
  'weighted_degree': 82,
  'betweenness': 0.0,
  'modularity_class': 1},
 'Jon Arryn': {'degree': 2,
  'weighted_degree': 11,
  'betweenness': 0.0005764583325230229,
  'modularity_class': 3},
 'Lancel': {'degree': 1,
  'weighted_degree': 7,
  'betweenness': 0.0,
  'modularity_class': 0},
 'Olenna': {'degree': 2,
  'weighted_degree': 12,
  'betweenness': 0.0,
  'modularity_class': 0},
 'Marillion': {'degree': 3,
  'weighted_degree': 23,
  'betweenness': 0.0,
  'modularity_class': 2},
 'Robert Arryn': {'degree': 3,
  'weighted_degree': 19,
  'betweenness': 0.0,
  'modularity_class': 2},
 'Ellaria': {'degree': 2,
  'weighted_degree': 10,
  'betweenness': 0.0,
  'modularity_class': 0},
 'Mace': {'degree': 3,
  'weighted_degree': 20,
  'betweenness': 0.0,
  'modularity_class': 0},
 'Rickard': {'degree': 2,
  'weighted_degree': 11,
  'betweenness': 0.0,
  'modularity_class': 2},
 'Ramsay': {'degree': 1,
  'weighted_degree': 4,
  'betweenness': 0.0,
  'modularity_class': 2},
 'Chataya': {'degree': 2,
  'weighted_degree': 9,
  'betweenness': 0.0,
  'modularity_class': 0},
 'Shireen': {'degree': 1,
  'weighted_degree': 5,
  'betweenness': 0.0,
  'modularity_class': 1},
 'Doran': {'degree': 1,
  'weighted_degree': 5,
  'betweenness': 0.0,
  'modularity_class': 0},
 'Walton': {'degree': 1,
  'weighted_degree': 10,
  'betweenness': 0.0,
  'modularity_class': 0}}

…and then transposing it (flipping the columns and rows) with .T

nodes_df = pd.DataFrame(dict(G.nodes(data=True))).T
nodes_df

	degree	weighted_degree	betweenness	modularity_class
Aemon	5.0	74.0	0.004623	1.0
Grenn	4.0	81.0	0.000060	1.0
Samwell	15.0	282.0	0.032273	1.0
Aerys	4.0	37.0	0.000000	0.0
Jaime	24.0	372.0	0.099943	0.0
Robert	18.0	128.0	0.209452	3.0
Tyrion	36.0	551.0	0.197913	0.0
Tywin	22.0	204.0	0.065538	0.0
Alliser	3.0	29.0	0.000000	1.0
Mance	12.0	160.0	0.007276	1.0
Amory	1.0	5.0	0.000000	0.0
Oberyn	7.0	76.0	0.019580	0.0
Arya	19.0	269.0	0.079607	2.0
Anguy	2.0	15.0	0.000000	4.0
Beric	6.0	75.0	0.000609	4.0
Bran	14.0	344.0	0.063017	2.0
Brynden	8.0	55.0	0.004727	2.0
Cersei	20.0	226.0	0.026746	2.0
Gendry	4.0	59.0	0.000000	4.0
Gregor	12.0	117.0	0.005880	0.0
Joffrey	18.0	255.0	0.025434	0.0
Jon	26.0	442.0	0.229965	1.0
Rickon	6.0	81.0	0.000763	2.0
Roose	4.0	17.0	0.000263	2.0
Sandor	13.0	137.0	0.014162	0.0
Thoros	5.0	60.0	0.001926	4.0
Balon	6.0	29.0	0.000628	0.0
Loras	9.0	76.0	0.003781	0.0
Belwas	4.0	67.0	0.018868	3.0
Barristan	6.0	63.0	0.040114	3.0
Illyrio	1.0	10.0	0.000000	3.0
Hodor	4.0	177.0	0.002194	1.0
Jojen	4.0	125.0	0.000406	1.0
Luwin	2.0	8.0	0.000000	2.0
Meera	5.0	139.0	0.001992	1.0
Nan	2.0	18.0	0.000000	2.0
Theon	4.0	38.0	0.000472	2.0
Brienne	7.0	122.0	0.001538	2.0
Bronn	4.0	59.0	0.000105	0.0
Podrick	5.0	64.0	0.000836	0.0
Lothar	5.0	34.0	0.000660	2.0
Walder	8.0	87.0	0.003273	2.0
Catelyn	18.0	184.0	0.048906	2.0
Edmure	8.0	98.0	0.006317	2.0
Hoster	3.0	24.0	0.000243	2.0
Jeyne	2.0	28.0	0.000000	2.0
Lysa	10.0	108.0	0.015215	2.0
Petyr	7.0	89.0	0.001150	2.0
Robb	25.0	342.0	0.126964	2.0
Roslin	4.0	32.0	0.000068	2.0
Sansa	26.0	383.0	0.126720	2.0
Stannis	14.0	146.0	0.102700	1.0
Elia	5.0	29.0	0.002997	0.0
Ilyn	6.0	32.0	0.000000	0.0
Meryn	7.0	47.0	0.000259	0.0
Pycelle	4.0	24.0	0.000000	0.0
Shae	5.0	45.0	0.001252	0.0
Varys	7.0	49.0	0.000909	0.0
Craster	5.0	75.0	0.018868	1.0
Karl	1.0	6.0	0.000000	1.0
Daario	4.0	30.0	0.000060	3.0
Drogo	4.0	35.0	0.000060	3.0
Irri	4.0	33.0	0.000180	3.0
Daenerys	14.0	232.0	0.157203	3.0
Aegon	1.0	8.0	0.000000	3.0
Jorah	6.0	89.0	0.002330	3.0
Kraznys	1.0	10.0	0.000000	3.0
Missandei	2.0	30.0	0.000000	3.0
Rakharo	1.0	7.0	0.000000	3.0
Rhaegar	6.0	42.0	0.007313	3.0
Viserys	3.0	19.0	0.015644	3.0
Worm	1.0	14.0	0.000000	3.0
Davos	5.0	87.0	0.056065	1.0
Cressen	1.0	4.0	0.000000	1.0
Salladhor	1.0	16.0	0.000000	1.0
Eddard	12.0	108.0	0.019341	2.0
Eddison	3.0	24.0	0.000000	1.0
Gilly	4.0	69.0	0.000000	1.0
Qyburn	1.0	11.0	0.000000	0.0
Renly	8.0	55.0	0.004228	0.0
Tommen	5.0	31.0	0.000698	0.0
Janos	6.0	37.0	0.023559	1.0
Bowen	2.0	11.0	0.000000	1.0
Kevan	6.0	50.0	0.019220	0.0
Margaery	7.0	96.0	0.001097	0.0
Myrcella	4.0	18.0	0.000624	0.0
Dalla	3.0	21.0	0.000000	1.0
Melisandre	4.0	62.0	0.008609	1.0
Orell	1.0	6.0	0.000000	1.0
Qhorin	5.0	59.0	0.000264	1.0
Rattleshirt	4.0	44.0	0.000000	1.0
Styr	2.0	23.0	0.000000	1.0
Val	4.0	31.0	0.005898	1.0
Ygritte	4.0	82.0	0.000000	1.0
Jon Arryn	2.0	11.0	0.000576	3.0
Lancel	1.0	7.0	0.000000	0.0
Olenna	2.0	12.0	0.000000	0.0
Marillion	3.0	23.0	0.000000	2.0
Robert Arryn	3.0	19.0	0.000000	2.0
Ellaria	2.0	10.0	0.000000	0.0
Mace	3.0	20.0	0.000000	0.0
Rickard	2.0	11.0	0.000000	2.0
Ramsay	1.0	4.0	0.000000	2.0
Chataya	2.0	9.0	0.000000	0.0
Shireen	1.0	5.0	0.000000	1.0
Doran	1.0	5.0	0.000000	0.0
Walton	1.0	10.0	0.000000	0.0

nodes_df.sort_values(by='betweenness', ascending=False)

	degree	weighted_degree	betweenness	modularity_class
Jon	26.0	442.0	0.229965	1.0
Robert	18.0	128.0	0.209452	3.0
Tyrion	36.0	551.0	0.197913	0.0
Daenerys	14.0	232.0	0.157203	3.0
Robb	25.0	342.0	0.126964	2.0
Sansa	26.0	383.0	0.126720	2.0
Stannis	14.0	146.0	0.102700	1.0
Jaime	24.0	372.0	0.099943	0.0
Arya	19.0	269.0	0.079607	2.0
Tywin	22.0	204.0	0.065538	0.0
Bran	14.0	344.0	0.063017	2.0
Davos	5.0	87.0	0.056065	1.0
Catelyn	18.0	184.0	0.048906	2.0
Barristan	6.0	63.0	0.040114	3.0
Samwell	15.0	282.0	0.032273	1.0
Cersei	20.0	226.0	0.026746	2.0
Joffrey	18.0	255.0	0.025434	0.0
Janos	6.0	37.0	0.023559	1.0
Oberyn	7.0	76.0	0.019580	0.0
Eddard	12.0	108.0	0.019341	2.0
Kevan	6.0	50.0	0.019220	0.0
Belwas	4.0	67.0	0.018868	3.0
Craster	5.0	75.0	0.018868	1.0
Viserys	3.0	19.0	0.015644	3.0
Lysa	10.0	108.0	0.015215	2.0
Sandor	13.0	137.0	0.014162	0.0
Melisandre	4.0	62.0	0.008609	1.0
Rhaegar	6.0	42.0	0.007313	3.0
Mance	12.0	160.0	0.007276	1.0
Edmure	8.0	98.0	0.006317	2.0
Val	4.0	31.0	0.005898	1.0
Gregor	12.0	117.0	0.005880	0.0
Brynden	8.0	55.0	0.004727	2.0
Aemon	5.0	74.0	0.004623	1.0
Renly	8.0	55.0	0.004228	0.0
Loras	9.0	76.0	0.003781	0.0
Walder	8.0	87.0	0.003273	2.0
Elia	5.0	29.0	0.002997	0.0
Jorah	6.0	89.0	0.002330	3.0
Hodor	4.0	177.0	0.002194	1.0
Meera	5.0	139.0	0.001992	1.0
Thoros	5.0	60.0	0.001926	4.0
Brienne	7.0	122.0	0.001538	2.0
Shae	5.0	45.0	0.001252	0.0
Petyr	7.0	89.0	0.001150	2.0
Margaery	7.0	96.0	0.001097	0.0
Varys	7.0	49.0	0.000909	0.0
Podrick	5.0	64.0	0.000836	0.0
Rickon	6.0	81.0	0.000763	2.0
Tommen	5.0	31.0	0.000698	0.0
Lothar	5.0	34.0	0.000660	2.0
Balon	6.0	29.0	0.000628	0.0
Myrcella	4.0	18.0	0.000624	0.0
Beric	6.0	75.0	0.000609	4.0
Jon Arryn	2.0	11.0	0.000576	3.0
Theon	4.0	38.0	0.000472	2.0
Jojen	4.0	125.0	0.000406	1.0
Qhorin	5.0	59.0	0.000264	1.0
Roose	4.0	17.0	0.000263	2.0
Meryn	7.0	47.0	0.000259	0.0
Hoster	3.0	24.0	0.000243	2.0
Irri	4.0	33.0	0.000180	3.0
Bronn	4.0	59.0	0.000105	0.0
Roslin	4.0	32.0	0.000068	2.0
Daario	4.0	30.0	0.000060	3.0
Drogo	4.0	35.0	0.000060	3.0
Grenn	4.0	81.0	0.000060	1.0
Marillion	3.0	23.0	0.000000	2.0
Ramsay	1.0	4.0	0.000000	2.0
Doran	1.0	5.0	0.000000	0.0
Mace	3.0	20.0	0.000000	0.0
Shireen	1.0	5.0	0.000000	1.0
Robert Arryn	3.0	19.0	0.000000	2.0
Dalla	3.0	21.0	0.000000	1.0
Chataya	2.0	9.0	0.000000	0.0
Orell	1.0	6.0	0.000000	1.0
Ygritte	4.0	82.0	0.000000	1.0
Olenna	2.0	12.0	0.000000	0.0
Rattleshirt	4.0	44.0	0.000000	1.0
Rickard	2.0	11.0	0.000000	2.0
Lancel	1.0	7.0	0.000000	0.0
Styr	2.0	23.0	0.000000	1.0
Ellaria	2.0	10.0	0.000000	0.0
Ilyn	6.0	32.0	0.000000	0.0
Bowen	2.0	11.0	0.000000	1.0
Pycelle	4.0	24.0	0.000000	0.0
Aerys	4.0	37.0	0.000000	0.0
Alliser	3.0	29.0	0.000000	1.0
Amory	1.0	5.0	0.000000	0.0
Anguy	2.0	15.0	0.000000	4.0
Gendry	4.0	59.0	0.000000	4.0
Illyrio	1.0	10.0	0.000000	3.0
Luwin	2.0	8.0	0.000000	2.0
Nan	2.0	18.0	0.000000	2.0
Jeyne	2.0	28.0	0.000000	2.0
Karl	1.0	6.0	0.000000	1.0
Qyburn	1.0	11.0	0.000000	0.0
Aegon	1.0	8.0	0.000000	3.0
Kraznys	1.0	10.0	0.000000	3.0
Missandei	2.0	30.0	0.000000	3.0
Rakharo	1.0	7.0	0.000000	3.0
Worm	1.0	14.0	0.000000	3.0
Cressen	1.0	4.0	0.000000	1.0
Salladhor	1.0	16.0	0.000000	1.0
Eddison	3.0	24.0	0.000000	1.0
Gilly	4.0	69.0	0.000000	1.0
Walton	1.0	10.0	0.000000	0.0