R：大（？）visNetwork效果不佳

Question

我試圖代表護理網絡（disease -> hospital）。我使用visNetwork。以下是我的網絡中的一些細節：2540個頂點和15776個邊。 而只有500的邊緣是可讀： 允許這些圖的代碼之後有：在這裏只用1000的邊緣時，結果

visNetwork(nodes=vn_nodes,edges = vn_edges, height = "1000px",width="100%")%>% 
visPhysics(enabled = FALSE)%>% 
visLayout(randomSeed = 12) 

我打一點點的選項，但沒有結果。任何想法，使它有用或我需要改變包（我試過networkD3沒有真正滿意）？

編輯：（添加一些數據）

對於edges

>vn_nodes 
      label id title group 
3  INSEE06004 2469 123  I 
4  INSEE06088 2470 2393  I 
5  INSEE10387 2471 91  I 
6  INSEE13055 2472 75  I 
7  INSEE13056 2473 54  I 
8  INSEE13205 2474 4192  I 
9  INSEE14118 2475 443  I 
11 INSEE20004 2477 13  I 
12 INSEE20033 2478 32  I 
13 INSEE21231 2479 2309  I 
14 INSEE25056 2480 35  I 
15 INSEE28085 2481 48  I 
16 INSEE29019 2482 2266  I 
18 INSEE30189 2484 194  I 
19 INSEE33063 2485 3080  I 
20 INSEE34172 2486 5245  I 
21 INSEE35238 2487 3869  I 
22 INSEE37261 2488 2639  I 
23 INSEE38516 2489 2493  I 
25 INSEE44109 2491 3083  I 
26 INSEE49007 2492 1330  I 
27 INSEE51454 2493 1144  I 
28 INSEE54395 2494 1304  I 
30 INSEE54547 2496 1839  I 
31 INSEE56260 2497 412  I 
33 INSEE59183 2499 220  I 
34 INSEE59350 2500 7339  I 
35 INSEE59606 2501 351  I 
36 INSEE60340 2502 32  I 
37 INSEE62041 2503 706  I 
38 INSEE62160 2504 10  I 
39 INSEE62498 2505 1019  I 
40 INSEE63113 2506 1526  I 
41 INSEE64102 2507 50  I 
42 INSEE64445 2508 15  I 
43 INSEE67482 2509 3382  I 
44 INSEE69029 2510 2905  I 
45 INSEE71076 2511 71  I 
46 INSEE72181 2512 1312  I 
47 INSEE75112 2513 460  I 
48 INSEE75113 2514 342  I 
50 INSEE75115 2516 3333  I 
51 INSEE75118 2517 15  I 
52 INSEE75119 2518 6813  I 
53 INSEE78498 2519 84  I 
54 INSEE80021 2520 2378  I 
55 INSEE83050 2521 116  I 
56 INSEE83137 2522 285  I 
57 INSEE84007 2523 132  I 
58 INSEE86194 2524 1351  I 
59 INSEE89024 2525 68  I 
61 INSEE92024 2527  5  I 
62 INSEE92025 2528 101  I 
63 INSEE93010 2529 87  I 
64 INSEE94028 2530 560  I 
65 INSEE95500 2531 126  I 
66 INSEE97120 2532 464  I 
67 INSEE97209 2533 229  I 
69 INSEE97302 2535 389  I 
70 INSEE97311 2536 233  I 
71 INSEE97416 2537 895  I 
72 INSEE98735 2538 121  I 
73 INSEE98818 2539 44  I 
74  ORPHA10 35  3  O 
89  ORPHA100 374  3  O 
108 ORPHA100011 1510  1  O 
110 ORPHA100013 2179  1  O 
111 ORPHA100031 1038  1  O 
120 ORPHA100033 2461  1  O 
121 ORPHA100043 1946  1  O 
122 ORPHA100092 1170  1  O 
124 ORPHA1001 136  4  O 
140 ORPHA100973 27  7  O 
158 ORPHA100980 761  5  O 
171 ORPHA100981 1649  3  O 
175 ORPHA100982 1342  4  O 
181  ORPHA101 2142  2  O 
182 ORPHA101016 762  1  O 
192 ORPHA101023 289  1  O 
218 ORPHA101029 1912  1  O 
219 ORPHA101033 1708  1  O 
220 ORPHA101038 1010  1  O 
226 ORPHA101039 763  4  O 
232 ORPHA101063 764  1  O 
238 ORPHA101070 1511  2  O 
244 ORPHA101075 1512  1  O 
247 ORPHA101076 1513  1  O 
250 ORPHA101081 834  3  O 
265 ORPHA101082 1786  1  O 
267 ORPHA101088 1913  3  O 
268 ORPHA101090 1861  1  O 
270 ORPHA101097 1343  1  O 
272 ORPHA101150 1344  1  O 
275 ORPHA101330 765  1  O 
278 ORPHA101685 290  4  O 
329 ORPHA1018 1810  2  O 
330 ORPHA101944 1011  1  O 
334 ORPHA101950 766  1  O 
335 ORPHA1020 1391  1  O 
336 ORPHA102002 291  3  O 
355 ORPHA102009 1012  1  O 
358 ORPHA102010 767  1  O 
366 ORPHA102013 292  2  O 
368 ORPHA102283 28 31  O 
418 ORPHA102284 29  4  O 
469 ORPHA102285 293  1  O 
517 ORPHA102369 30 12  O 
555 ORPHA102373 1345  1  O 
556 ORPHA1027 2019  1  O 
557  ORPHA103 375  1  O 
573 ORPHA1031 1241  1  O 
578 ORPHA1034 137  1  O 
609 ORPHA1037 138  3  O 
627 ORPHA103918 1787  1  O 
628  ORPHA104 1043  3  O 
647 ORPHA104003 2127  1  O 
648 ORPHA104007 1171  1  O 
651 ORPHA104009 2128  1  O 
653 ORPHA104010 1788  1  O 
656 ORPHA104013 1789  1  O 
657 ORPHA104075 2288  1  O 
658 ORPHA1041 890  3  O 
674 ORPHA1046 1811  1  O 
675 ORPHA1047 1555  1  O 
676 ORPHA1048 891  4  O 
697  ORPHA105 1356  1  O 
704 ORPHA1052 325  1  O 
723 ORPHA1053 1556  1  O 
726 ORPHA1054 494  1  O 
731 ORPHA1055 1392  1  O 
732 ORPHA1057 1242  1  O 
735 ORPHA1059 1670  1  O 
738  ORPHA106 7  7  O 
789 ORPHA1062 1671  1  O 
790 ORPHA1064 2218  1  O 
791 ORPHA1065 1557  1  O 
796 ORPHA1068 2260  1  O 
797  ORPHA107 48 12  O 
830 ORPHA1071 345 10  O 
844 ORPHA1072 1393  1  O 
846 ORPHA1081 495  1  O 
848 ORPHA1083 496  1  O 
853 ORPHA1084 1558  3  O 
856 ORPHA1088 2429  1  O 
857 ORPHA108959 1709  1  O 
859 ORPHA108961 1990  1  O 
860 ORPHA108963 1013  1  O 
861 ORPHA108967 1014  1  O 
865 ORPHA108969 1947  2  O 
868 ORPHA108971 1514  1  O 
872 ORPHA108973 2129  1  O 
873 ORPHA108977 294  1  O 
877 ORPHA108979 1991  1  O 
878 ORPHA108981 768  1  O 
890 ORPHA108983 1650  1  O 
892 ORPHA108985 355  1  O 
898 ORPHA108989 1515  1  O 
910 ORPHA108991 1172  2  O 
913  ORPHA109 376  1  O 
923 ORPHA109007 295  3  O 
940 ORPHA109009 769  1  O 
946 ORPHA109011 296  1  O 
969  ORPHA11 836  1  O 
972  ORPHA110 49  1  O 
1003 ORPHA1104 1243  1  O 
1004 ORPHA111 1044  2  O 
1010 ORPHA1114 497  1  O 
1017 ORPHA1117 2313  2  O 
1018 ORPHA1118 1953  1  O 
1021 ORPHA112 50  2  O 
1033 ORPHA1125 1077  1  O 
1039 ORPHA1126 1672  1  O 
1040 ORPHA113 1207  1  O 
1042 ORPHA1132 498  2  O 
1045 ORPHA1134 1078  1  O 
1046 ORPHA1135 2219  1  O 
1047 ORPHA1136 1394  1  O 
1061 ORPHA1138 1673  1  O 
1062 ORPHA1143 1079  1  O 

Answer 1

我不能用排斥，因爲我已經停用

FWIW，這裏是您可以嘗試所有different layout algorithms及其參數化：

library(igraph) 
library(visNetwork) 
library(qgraph) 
g <- graph_from_data_frame(vn_edges) 
V(g)$color <- bipartite.mapping(g)$type + 1L 
V(g)$title <- V(g)$name 
set.seed(1) 
coords <- qgraph.layout.fruchtermanreingold(
    as_edgelist(g, names = F), 
    weights=E(g)$value, 
    vcount=vcount(g), 
    area=vcount(g)^2, 
    repulse.rad=vcount(g)^3 
) 
visIgraph(g) %>% 
    visEdges(width = "value", title="value", color = list(highlight="#ff0000", opacity = .8)) %>% 
    visIgraphLayout("layout.norm", layoutMatrix = coords) 

Answer 2

您可以定義碰撞和其他選項，以便標籤不會覆蓋scaling.label.enabled

檢查所有從很好的網絡文檔選項。 enter link description here