足球网络

加载GML格式的足球网络并计算一些网络统计数据。

演示如何下载压缩文件中的GML图，解压缩并加载到NetworkX图中。

需要互联网连接以下载URL http://www-personal.umich.edu/~mejn/netdata/football.zip

The file football.gml contains the network of American football games
between Division IA colleges during regular season Fall 2000, as compiled
by M. Girvan and M. Newman.  The nodes have values that indicate to which
conferences they belong.  The values are as follows:

  0 = Atlantic Coast
  1 = Big East
  2 = Big Ten
  3 = Big Twelve
  4 = Conference USA
  5 = Independents
  6 = Mid-American
  7 = Mountain West
  8 = Pacific Ten
  9 = Southeastern
 10 = Sun Belt
 11 = Western Athletic

If you make use of these data, please cite M. Girvan and M. E. J. Newman,
Community structure in social and biological networks,
Proc. Natl. Acad. Sci. USA 99, 7821-7826 (2002).

Correction: Two edges were erroneously duplicated in this data set, and
have been removed (21 SEP 2014)

BrighamYoung         12
FloridaState         12
Iowa                 12
KansasState          12
NewMexico            11
TexasTech            12
PennState            12
SouthernCalifornia   12
ArizonaState         11
SanDiegoState        11
Baylor               10
NorthTexas           10
NorthernIllinois     10
Northwestern         11
WesternMichigan      10
Wisconsin            12
Wyoming              11
Auburn               11
Akron                11
VirginiaTech         11
Alabama              11
UCLA                 11
Arizona              11
Utah                 11
ArkansasState        10
NorthCarolinaState   11
BallState            10
Florida              11
BoiseState            9
BostonCollege        11
WestVirginia         11
BowlingGreenState    11
Michigan             11
Virginia             10
Buffalo              11
Syracuse             11
CentralFlorida        8
GeorgiaTech          11
CentralMichigan      11
Purdue               11
Colorado             11
ColoradoState        10
Connecticut           7
EasternMichigan      11
EastCarolina         11
Duke                 11
FresnoState          11
OhioState            11
Houston              11
Rice                 11
Idaho                 9
Washington           11
Kansas               10
SouthernMethodist    12
Kent                 10
Pittsburgh           11
Kentucky             10
Louisville           10
LouisianaTech        10
LouisianaMonroe       8
Minnesota            11
MiamiOhio            11
Vanderbilt           11
MiddleTennesseeState  9
Illinois             11
MississippiState     11
Memphis              11
Nevada               12
Oregon               11
NewMexicoState       11
SouthCarolina        11
Ohio                 10
IowaState            11
SanJoseState         11
Nebraska             11
SouthernMississippi  10
Tennessee            11
Stanford             11
WashingtonState      11
Temple               11
Navy                 11
TexasA&M             11
NotreDame            11
TexasElPaso          11
Oklahoma             11
Toledo                9
Tulane               11
Mississippi          11
Tulsa                12
NorthCarolina        11
UtahState             9
Army                 11
Cincinnati           11
AirForce             10
Rutgers              10
Georgia              10
LouisianaState       10
LouisianaLafayette    8
Texas                11
Marshall             10
MichiganState        11
MiamiFlorida         10
Missouri             10
Clemson              10
NevadaLasVegas       12
WakeForest           10
Indiana              11
OklahomaState        10
OregonState          10
Maryland             11
TexasChristian       11
California           11
AlabamaBirmingham    10
Arkansas             10
Hawaii               11

import urllib.request
import io
import zipfile

import matplotlib.pyplot as plt
import networkx as nx

url = "http://www-personal.umich.edu/~mejn/netdata/football.zip"

sock = urllib.request.urlopen(url)  # open URL
s = io.BytesIO(sock.read())  # read into BytesIO "file"
sock.close()

zf = zipfile.ZipFile(s)  # zipfile object
txt = zf.read("football.txt").decode()  # read info file
gml = zf.read("football.gml").decode()  # read gml data
# 丢弃带有 # 的虚假第一行，来自 mejn 文件
gml = gml.split("\n")[1:]
G = nx.parse_gml(gml)  # parse gml data

print(txt)
# 打印每个队伍的比赛场次数
for n, d in G.degree():
    print(f"{n:20} {d:2}")

options = {"node_color": "black", "node_size": 50, "linewidths": 0, "width": 0.1}

pos = nx.spring_layout(G, seed=1969)  # 用于可重复布局的种子
nx.draw(G, pos, **options)
plt.show()