如何解决如何将networkx节点位置转换为python-igraph节点位置布局
我有一个节点位置字典（一个布局）node_pos_dict（在这篇文章的底部定义），节点 id 作为键，节点位置作为值。为简单起见，我省略了边缘。我正在使用它在 G 中绘制图表 networkx：
import networkx as nx
import matplotlib.pyplot as plt
G = nx.Graph()
nodes = [i for i in range(100)]
G.add_nodes_from(nodes)
fig,ax = plt.subplots(figsize=(8,10))
nx.draw_networkx(G,pos=node_pos_dict,ax=ax,with_labels=False)
plt.axis('off')
plt.tight_layout()
plt.show()
这给出了以下图像：
我试图在 python-igraph 图中获得相同的布局，以便使用该包中可用的某些社区突出显示功能。
node_pos_dict 字典定义为：
node_pos_dict = {0: (144.3188,119.44731),1: (124.09237,-33.596596),2: (-101.445206,-230.88632),3: (161.83209,0.38454914),4: (-82.14839,-245.6368),5: (-85.32251,-206.46722),6: (151.14572,82.951904),7: (186.52087,104.135445),8: (-72.2828,-222.62256),9: (180.5292,-25.867607),10: (113.72072,83.670166),11: (175.75494,134.25246),12: (-98.248184,116.63043),13: (198.58878,123.65588),14: (-132.95235,4.804117),15: (53.636906,25.208912),16: (-96.807945,-118.86241),17: (-42.95986,-150.97101),18: (145.22792,-64.54069),19: (179.72614,60.22186),20: (125.6956,-118.42535),21: (-110.77464,-162.60362),22: (-117.83106,26.956175),23: (-117.83714,-187.87215),24: (-95.32954,232.9699),25: (34.003876,321.52094),26: (-68.26795,295.06512),27: (-63.048847,239.11066),28: (-75.68445,-28.730654),29: (75.17831,-75.029854),30: (-28.244192,-19.343128),31: (-56.09895,-63.656025),32: (-66.53724,-173.97981),33: (-91.61079,-144.26376),34: (-7.710302,236.83008),35: (92.75697,-83.03888),36: (-40.09295,73.20203),37: (-142.14711,-26.725447),38: (3.575774,17.843548),39: (-91.95948,77.86012),40: (200.68674,89.53631),41: (0.8394178,-107.49954),42: (12.500817,-50.1444),43: (-127.56198,149.43623),44: (-11.264021,-41.274815),45: (47.784233,-134.48622),46: (7.4685154,-76.65924),47: (194.3162,23.981865),48: (212.50543,70.98608),49: (153.31735,43.914085),50: (-133.44196,114.36704),51: (5.701025,85.38443),52: (13.946121,153.68181),53: (-25.266066,187.4926),54: (25.329966,294.0766),55: (-85.00704,266.47177),56: (-61.753925,168.47987),57: (-56.519913,272.79797),58: (-21.517618,265.3851),59: (-48.90309,-5.526082),60: (-109.32565,-28.847795),61: (-56.12136,-131.05943),62: (-19.714733,-123.95303),63: (10.257481,316.74423),64: (-92.10561,46.171707),65: (38.559624,259.36142),66: (9.978462,117.79648),67: (-25.08352,-97.375275),68: (40.990887,213.83508),69: (-14.715101,145.84436),70: (11.447578,271.5689),71: (64.48909,-109.939255),72: (94.36185,-137.68431),73: (9.287598,172.41864),74: (20.429993,-143.1729),75: (133.95541,18.159035),76: (-123.929146,-47.133484),77: (-31.405153,-173.0785),78: (19.702349,239.63599),79: (-120.557686,240.36055),80: (-108.39326,143.99583),81: (-88.96142,15.370992),82: (-159.7047,-13.08283),83: (-42.595707,230.62224),84: (180.81544,-148.1233),85: (162.917,-181.4351),86: (-0.9790172,293.15427),87: (143.01433,-204.42032),88: (191.59567,-170.5149),89: (157.8586,-37.603153),90: (167.32344,-212.64957),91: (59.633255,209.17967),92: (187.9868,-198.9159),93: (147.55754,-155.44865),94: (146.49014,-131.13498),95: (73.7772,229.48337),96: (-53.127087,-105.21127),97: (-114.917305,264.62866),98: (127.29826,-183.27467),99: (-27.884693,111.62785)}
这个字典与使用任何 networkx 的内置布局算法获得的字典非常相似，例如Kamada-Kawai 布局：
alt_node_pos_dict = nx.kamada_kawai_layout(G)
alt_node_pos_dict = {0: array([ 9.99999996e-01,-2.42300517e-09]),1: array([0.9980267,0.06279052]),2: array([0.99211487,0.12533325]),3: array([0.98228704,0.18738127]),4: array([0.96858321,0.24868989]),5: array([0.95105666,0.30901704]),6: array([0.92977645,0.36812454]),7: array([0.90482706,0.4257793 ]),8: array([0.87630662,0.48175364]),9: array([0.84432796,0.53582681]),10: array([0.809017,0.58778526]),11: array([0.77051321,0.63742396]),12: array([0.72896867,0.68454714]),13: array([0.68454714,0.72896867]),14: array([0.63742397,0.77051322]),15: array([0.58778529,0.80901704]),16: array([0.53582671,0.84432777]),17: array([0.48175365,0.87630665]),18: array([0.42577934,0.90482715]),19: array([0.36812454,0.92977645]),20: array([0.30901702,0.95105659]),21: array([0.24868991,0.96858329]),22: array([0.18738128,0.98228708]),23: array([0.12533325,0.99211486]),24: array([0.06279052,0.9980267 ]),25: array([5.77200179e-09,1.00000000e+00]),26: array([-0.06279052,27: array([-0.12533326,0.99211487]),28: array([-0.18738128,0.98228709]),29: array([-0.24868993,0.96858333]),30: array([-0.30901695,0.9510564 ]),31: array([-0.36812456,0.92977649]),32: array([-0.42577926,0.90482702]),33: array([-0.48175365,0.87630662]),34: array([-0.53582694,0.84432814]),35: array([-0.58778513,0.80901682]),36: array([-0.63742402,0.77051328]),37: array([-0.6845472,0.72896873]),38: array([-0.72896847,0.68454697]),39: array([-0.77051329,0.63742405]),40: array([-0.80901708,0.58778534]),41: array([-0.84432783,0.53582676]),42: array([-0.87630683,0.48175374]),43: array([-0.90482693,0.42577922]),44: array([-0.92977668,0.36812462]),45: array([-0.95105635,0.30901694]),46: array([-0.96858329,0.24868992]),47: array([-0.98228706,0.18738128]),48: array([-0.99211486,0.12533326]),49: array([-0.99802669,0.06279053]),50: array([-9.99999983e-01,1.60126757e-08]),51: array([-0.99802675,-0.0627905 ]),52: array([-0.9921147,-0.12533324]),53: array([-0.98228737,-0.18738135]),54: array([-0.96858307,-0.24868987]),55: array([-0.95105661,-0.30901703]),56: array([-0.92977645,-0.36812454]),57: array([-0.90482715,-0.42577933]),58: array([-0.87630663,-0.48175364]),59: array([-0.84432783,-0.53582672]),60: array([-0.80901714,-0.58778534]),61: array([-0.77051324,-0.637424 ]),62: array([-0.72896872,-0.6845472 ]),63: array([-0.68454696,-0.72896848]),64: array([-0.63742394,-0.77051313]),65: array([-0.58778527,-0.80901696]),66: array([-0.53582677,-0.84432793]),67: array([-0.48175369,-0.87630674]),68: array([-0.4257793,-0.90482711]),69: array([-0.36812453,-0.92977647]),70: array([-0.30901697,-0.95105646]),71: array([-0.24868993,-0.96858336]),72: array([-0.18738128,-0.98228711]),73: array([-0.12533326,-0.99211489]),74: array([-0.06279052,-0.99802671]),75: array([-5.68942479e-09,-9.99999986e-01]),76: array([ 0.06279051,-0.99802672]),77: array([ 0.12533325,-0.99211492]),78: array([ 0.18738129,-0.98228721]),79: array([ 0.24868982,-0.96858298]),80: array([ 0.30901699,-0.95105657]),81: array([ 0.36812451,-0.92977645]),82: array([ 0.42577932,-0.90482717]),83: array([ 0.48175365,-0.87630669]),84: array([ 0.53582682,-0.84432792]),85: array([ 0.58778521,-0.80901689]),86: array([ 0.63742404,-0.77051328]),87: array([ 0.68454717,-0.72896867]),88: array([ 0.72896867,-0.68454713]),89: array([ 0.77051321,-0.63742395]),90: array([ 0.80901698,-0.58778523]),91: array([ 0.8443279,-0.53582677]),92: array([ 0.87630668,-0.48175367]),93: array([ 0.9048271,-0.42577932]),94: array([ 0.92977651,-0.36812457]),95: array([ 0.95105659,96: array([ 0.96858306,97: array([ 0.9822874,-0.18738136]),98: array([ 0.99211474,-0.12533326]),99: array([ 0.99802665,-0.06279054])}
区别在于 alt_node_pos_dict 包含坐标数组，而不是 node_pos_dict 中的元组。
解决方法
为了在igraph中获得相同的布局：
import igraph as ig
H = ig.Graph.from_networkx(G)
node_pos_list = [[node_pos_dict[node][0],-node_pos_dict[node][1]] for node in H.vs['_nx_name']]
ig.plot(H_ig,layout=node_pos_list,bbox=(800,1000)).show()
这给出了以下图像：
我相信 networkx 的后端绘图库是 matplolib，它使用大小和 dpi，而 python-igraph 使用像素。这就是为什么您需要设置 bbox=(800,1000)（或具有相同比率的其他值）以获得与问题中的原始 figsize=(8,10) 相同的纵横比。另一个需要注意的重要事项是创建 node_pos_list 时 y 坐标中的减号：
node_pos_list = [[node_pos_dict[node][0],-node_pos_dict[node][1]] for node in H.vs['_nx_name']]
如果不包含减号，则绘图反转：
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