concern
I've been working on graph learning lately and found problems while plotting with the networkx library.
'''
author：zheng
time：2020.10.23
'''
import networkx as nx
import random
g = nx.karate_club_graph() # Karate club
for u,v in :
print(u,v)
g.add_edge(u, v, weight=(0, 1)) # Random numbers with weights between (0,1)
print(())
output result
[(0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), (0, 8), (0, 10), (0, 11), (0, 12), (0, 13), (0, 17), (0, 19), (0, 21), (0, 31), (1, 2), (1, 3), (1, 7), (1, 13), (1, 17), (1, 19), (1, 21), (1, 30), (2, 3), (2, 7), (2, 8), (2, 9), (2, 13), (2, 27), (2, 28), (2, 32), (3, 7), (3, 12), (3, 13), (4, 6), (4, 10), (5, 6), (5, 10), (5, 16), (6, 16), (8, 30), (8, 32), (8, 33), (13, 33), (19, 33), (31, 24), (31, 25), (31, 28), (31, 32), (31, 33), (30, 32), (30, 33), (9, 33), (27, 23), (27, 24), (27, 33), (28, 33), (32, 14), (32, 15), (32, 18), (32, 20), (32, 22), (32, 23), (32, 29), (32, 33), (33, 14), (33, 15), (33, 18), (33, 20), (33, 22), (33, 23), (33, 26), (33, 29), (23, 25), (23, 29), (25, 24), (29, 26)]
Found the problem, I obviously set the weights randomly by (0, 1) why it is not shown in the result output, is it the problem of input weights or the problem of result display.
'''
author：zheng
time：2020.10.23
'''
import networkx as nx
import random
g = nx.karate_club_graph() # Karate club
for u,v in :
g.add_edge(u, v, weight=(0, 1)) # Random numbers with weights between (0,1)
print((data=True))
Let's see if there is any difference between the two codes in(data=True)has been added to thedata=True
Output the result at this point:
[(0, 1, {'weight': 0.49899129531032826}), (0, 2, {'weight': 0.7493395367183026}), (0, 3, {'weight': 0.9805046801748599}), (0, 4, {'weight': 0.644560549909913}), (0, 5, {'weight': 0.022461095194206915}), (0, 6, {'weight': 0.39855273941801683}), (0, 7, {'weight': 0.9167666610641618}), (0, 8, {'weight': 0.3736839965822629}), (0, 10, {'weight': 0.1685687039463848}), (0, 11, {'weight': 0.5900599708379352}), (0, 12, {'weight': 0.49772285717726605}), (0, 13, {'weight': 0.6988903320924684}), (0, 17, {'weight': 0.8108991409995218}), (0, 19, {'weight': 0.21743421569163335}), (0, 21, {'weight': 0.687637570308398}), (0, 31, {'weight': 0.13180440967486262}), (1, 2, {'weight': 0.0603379086168323}), (1, 3, {'weight': 0.9536653778354264}), (1, 7, {'weight': 0.1680232359702576}), (1, 13, {'weight': 0.23821372652905115}), (1, 17, {'weight': 0.6861169007257469}), (1, 19, {'weight': 0.006553274592374314}), (1, 21, {'weight': 0.23452495215883118}), (1, 30, {'weight': 0.7638165639559286}), (2, 3, {'weight': 0.18381620307197954}), (2, 7, {'weight': 0.08671998389998026}), (2, 8, {'weight': 0.7395899045684956}), (2, 9, {'weight': 0.5973616237830935}), (2, 13, {'weight': 0.25253256663029156}), (2, 27, {'weight': 0.4151629971620948}), (2, 28, {'weight': 0.6830413630275037}), (2, 32, {'weight': 0.10877354662752325}), (3, 7, {'weight': 0.3165078261209674}), (3, 12, {'weight': 0.3258985972202395}), (3, 13, {'weight': 0.5617183737707032}), (4, 6, {'weight': 0.9944831897451706}), (4, 10, {'weight': 0.4258447405573552}), (5, 6, {'weight': 0.17102663345956715}), (5, 10, {'weight': 0.41020894392823837}), (5, 16, {'weight': 0.24048864347638477}), (6, 16, {'weight': 0.5401785263069063}), (8, 30, {'weight': 0.4604358340149278}), (8, 32, {'weight': 0.9601569527970788}), (8, 33, {'weight': 0.2905405465193912}), (13, 33, {'weight': 0.2556445407164615}), (19, 33, {'weight': 0.3008126988319231}), (31, 24, {'weight': 0.8781944129721222}), (31, 25, {'weight': 0.392828914742127}), (31, 28, {'weight': 0.7410701847068474}), (31, 32, {'weight': 0.39869250595380246}), (31, 33, {'weight': 0.4380052794486696}), (30, 32, {'weight': 0.4587792580500568}), (30, 33, {'weight': 0.5106934704075864}), (9, 33, {'weight': 0.9037424067215868}), (27, 23, {'weight': 0.9151325306454512}), (27, 24, {'weight': 0.6079907996445639}), (27, 33, {'weight': 0.6168782680542676}), (28, 33, {'weight': 0.9529880704286767}), (32, 14, {'weight': 0.21711370788129514}), (32, 15, {'weight': 0.21906480255644156}), (32, 18, {'weight': 0.36297161231472697}), (32, 20, {'weight': 0.8295507296873654}), (32, 22, {'weight': 0.725850047579389}), (32, 23, {'weight': 0.06395474428944792}), (32, 29, {'weight': 0.021001018687274553}), (32, 33, {'weight': 0.29227780907194645}), (33, 14, {'weight': 0.7898337840851372}), (33, 15, {'weight': 0.06574640956244104}), (33, 18, {'weight': 0.3193055980182168}), (33, 20, {'weight': 0.22814267912232755}), (33, 22, {'weight': 0.934928086748862}), (33, 23, {'weight': 0.8780586608909188}), (33, 26, {'weight': 0.834765093283264}), (33, 29, {'weight': 0.8927802653939352}), (23, 25, {'weight': 0.18106036608743914}), (23, 29, {'weight': 0.7824721548411848}), (25, 24, {'weight': 0.9362577071184671}), (29, 26, {'weight': 0.06557785001633887})]
How to output only weights
import networkx as nx
import random
g = nx.karate_club_graph() # Karate club
for u,v in :
g.add_edge(u, v, weight=(0, 1)) # The weights are (0,1)
for (u,v,d) in (data=True):
print(d['weight'])
output result
0.9175521740544361
0.09841104142600388
0.9557658899707079
0.9256010898041206
0.2519120041349847
0.48370396192288767
0.8354304958648846
0.758094795660556
0.7910256982243447
0.6281003207621544
0.9801420646231339
0.7941450155753779
0.3851720075568309
0.802202234860892
0.7923045754263267
0.5270583359776736
0.9523963539542339
0.7474601472346581
0.044707615637251674
0.5349188097983026
0.6158693844408302
0.9456154478628968
0.7547788968185274
0.5648525235741113
0.6657063624514532
0.3109915743055601
0.3969190047820317
0.8763009836310122
0.7101598558464499
0.012225959063178693
0.700579386399397
0.8304116006624506
0.426518724548162
0.07244870577629914
0.36116795615537345
0.45781457416039606
0.25726914791707645
0.29778955309109023
0.8892096639219873
0.39322230058450647
0.5085017515323529
0.9597980742524421
0.08034618164792517
0.9143712112937563
0.17242150180445381
0.8914706349104955
0.8480034205451665
0.8217034225251223
0.45552196009659873
0.3909280195122691
0.45119988941609357
0.02984583822414133
0.14404544949710196
0.45459370924953857
0.10296953351890004
0.4948127850493056
0.9238669854480596
0.9399144983422378
0.919211279645529
0.24084759450828674
0.4410486851096309
0.7699702465967465
0.27749525807367836
0.9449097003790671
0.5019309896062647
0.42774455164796255
0.43988066338230847
0.7405733579782761
0.2308870299365694
0.12306785713306911
0.7139426386075743
0.2640769424119722
0.031149630992576394
0.07700734539599274
0.37034537464573547
0.7034898163898959
0.8557141929947621
0.06539918397508715
Above is networkx library draw weighted graph to unweighted graph weighted output details, more information about networkx with weighted graph unweighted graph output please pay attention to my other related articles!