[igraph] problem reading .net file

[Simone Gabbriellini]

Dear List,

I am experiencing a problem importing a .net file into igraph.
The sintax I am using is:

> g<-read.graph("primoMese2004.net", format="pajek")

when the file is imported and I plot it, I can see that the node  
labels are switched, and the more central nodes (based on degree  
centrality) labelled "16", "1", "50" that I had before become "2",  
"30" and "45"...

maybe I do something wrong in the sintax?

the .net file is:

*Vertices 64
1 "47"
2 "19"
3 "1"
4 "57"
5 "8"
6 "21"
7 "49"
8 "2"
9 "33"
10 "4"
11 "36"
12 "64"
13 "0"
14 "45"
15 "3"
16 "6"
17 "42"
18 "54"
19 "31"
20 "27"
21 "14"
22 "17"
23 "10"
24 "23"
25 "12"
26 "61"
27 "35"
28 "22"
29 "5"
30 "24"
31 "16"
32 "37"
33 "11"
34 "56"
35 "29"
36 "18"
37 "48"
38 "13"
39 "41"
40 "58"
41 "9"
42 "32"
43 "46"
44 "44"
45 "59"
46 "50"
47 "43"
48 "53"
49 "15"
50 "62"
51 "38"
52 "63"
53 "60"
54 "7"
55 "51"
56 "34"
57 "30"
58 "26"
59 "55"
60 "28"
61 "39"
62 "52"
63 "25"
64 "20"
*Arcs
41 15 1.0
46 20 1.0
3 23 2.0
22 5 1.0
47 31 2.0
57 47 2.0
3 9 1.0
3 17 1.0
29 58 1.0
46 46 4.0
46 24 1.0
3 12 1.0
8 21 1.0
3 19 1.0
25 23 6.0
35 23 2.0
60 60 3.0
3 33 1.0
2 10 1.0
47 5 1.0
29 16 1.0
31 28 1.0
15 44 1.0
29 63 1.0
8 23 5.0
41 25 5.0
31 49 1.0
13 41 1.0
3 6 1.0
25 5 1.0
7 41 1.0
3 20 2.0
8 38 1.0
41 6 2.0
60 24 2.0
63 46 1.0
5 23 1.0
3 43 1.0
29 3 3.0
6 41 2.0
2 44 1.0
46 31 2.0
24 63 1.0
31 15 1.0
31 64 1.0
10 41 3.0
41 3 1.0
10 25 2.0
41 41 4.0
23 30 1.0
29 57 1.0
3 26 1.0
63 24 3.0
41 2 10.0
3 41 2.0
3 31 2.0
46 44 1.0
47 2 2.0
31 4 1.0
8 31 8.0
22 3 1.0
25 30 1.0
23 6 1.0
24 24 2.0
3 38 2.0
5 41 1.0
41 22 3.0
23 58 1.0
46 35 1.0
21 2 2.0
63 44 1.0
31 53 1.0
3 3 1.0
44 10 2.0
46 5 2.0
2 21 2.0
57 6 1.0
44 30 1.0
35 22 1.0
5 25 1.0
38 3 2.0
31 62 1.0
3 29 2.0
28 47 1.0
46 26 1.0
25 21 1.0
31 34 1.0
46 27 1.0
6 44 1.0
60 2 1.0
46 15 1.0
54 10 1.0
41 23 10.0
46 62 1.0
3 36 1.0
46 51 1.0
5 47 1.0
2 58 6.0
15 41 1.0
6 54 1.0
2 2 9.0
41 54 1.0
23 41 8.0
47 44 1.0
31 16 1.0
3 25 2.0
23 22 3.0
31 19 1.0
3 40 1.0
5 6 1.0
3 59 1.0
31 22 2.0
46 16 1.0
23 24 4.0
41 38 1.0
41 60 1.0
21 3 1.0
25 38 2.0
8 44 1.0
25 57 1.0
3 2 2.0
3 64 1.0
23 63 1.0
3 45 1.0
49 58 1.0
46 36 1.0
31 30 1.0
31 23 4.0
31 31 9.0
31 33 1.0
31 26 1.0
30 23 1.0
41 5 1.0
25 41 5.0
46 37 1.0
2 46 1.0
31 6 1.0
44 44 5.0
2 3 1.0
8 25 2.0
8 28 1.0
3 34 1.0
3 63 2.0
31 14 1.0
28 22 1.0
60 57 1.0
31 52 1.0
3 62 1.0
31 61 1.0
3 46 1.0
25 22 1.0
31 56 1.0
13 60 1.0
31 40 1.0
49 23 2.0
10 47 1.0
8 30 1.0
44 7 3.0
47 47 4.0
22 41 3.0
57 41 1.0
41 24 2.0
63 58 1.0
41 46 1.0
3 15 2.0
44 47 1.0
23 47 1.0
6 10 1.0
10 46 2.0
30 25 1.0
47 27 1.0
6 25 2.0
44 24 1.0
46 23 2.0
31 20 1.0
63 31 1.0
8 24 2.0
3 4 1.0
8 60 3.0
3 1 1.0
3 5 1.0
47 41 3.0
3 44 1.0
46 4 1.0
29 46 2.0
31 21 1.0
31 58 2.0
44 6 1.0
23 28 2.0
3 42 1.0
29 10 2.0
10 29 4.0
15 60 1.0
8 5 1.0
22 22 3.0
44 2 1.0
30 2 2.0
31 63 2.0
8 46 2.0
41 29 6.0
63 25 1.0
31 50 1.0
30 3 1.0
16 47 1.0
35 29 1.0
31 29 7.0
21 24 1.0
3 18 1.0
15 23 1.0
60 10 1.0
24 2 1.0
22 31 1.0
63 29 1.0
6 23 2.0
46 12 1.0
3 21 2.0
63 23 3.0
31 57 1.0
31 39 1.0
38 41 1.0
2 41 3.0
47 37 1.0
46 64 1.0
44 41 4.0
47 15 1.0
24 23 3.0
46 52 1.0
31 27 1.0
23 23 4.0
58 10 2.0
24 60 2.0
3 49 2.0
23 2 4.0
23 38 1.0
60 54 1.0
5 54 1.0
15 2 1.0
57 44 1.0
46 45 1.0
38 24 1.0
8 47 1.0
3 58 1.0
41 31 4.0
46 2 2.0
29 60 4.0
25 29 3.0
23 25 3.0
29 2 1.0
5 46 1.0
60 32 1.0
8 63 1.0
54 54 1.0
31 12 1.0
3 32 1.0
23 31 3.0
31 9 1.0
46 59 1.0
10 23 5.0
41 13 1.0
3 60 1.0
46 18 1.0
57 60 4.0
29 25 2.0
23 46 1.0
46 32 1.0
29 31 3.0
54 41 1.0
31 7 2.0
31 10 4.0
58 63 1.0
3 56 1.0
58 29 1.0
44 15 1.0
31 54 1.0
60 31 1.0
44 63 1.0
8 22 1.0
31 3 3.0
41 10 4.0
57 23 4.0
29 29 1.0
31 37 1.0
60 16 1.0
2 29 2.0
7 10 1.0
46 14 1.0
46 11 1.0
23 5 1.0
46 6 1.0
31 41 5.0
41 47 7.0
24 44 1.0
2 15 1.0
3 61 1.0
31 51 1.0
47 16 1.0
25 25 2.0
31 17 1.0
46 1 1.0
25 3 1.0
58 31 1.0
16 60 1.0
31 11 1.0
21 25 3.0
25 63 1.0
24 38 1.0
46 25 1.0
3 50 1.0
31 48 1.0
46 28 1.0
44 25 3.0
30 38 1.0
46 60 1.0
31 5 1.0
29 41 2.0
24 41 1.0
23 35 2.0
22 28 1.0
46 22 1.0
30 44 1.0
2 60 2.0
8 10 1.0
10 44 3.0
3 7 1.0
31 55 1.0
3 10 2.0
46 10 3.0
46 3 1.0
10 31 5.0
46 30 1.0
15 47 1.0
31 1 1.0
46 34 2.0
31 43 1.0
22 35 1.0
44 57 1.0
31 38 1.0
58 58 2.0
5 10 1.0
57 29 2.0
6 57 1.0
28 3 1.0
46 9 1.0
47 28 1.0
22 23 2.0
10 6 1.0
8 41 6.0
46 48 1.0
46 33 1.0
31 25 2.0
10 54 1.0
44 23 2.0
46 56 1.0
10 7 1.0
46 17 1.0
31 35 1.0
2 30 1.0
3 22 2.0
46 57 1.0
23 15 1.0
10 58 3.0
24 3 1.0
3 51 1.0
60 23 1.0
60 15 1.0
25 6 2.0
46 42 1.0
31 60 3.0
46 41 2.0
46 19 1.0
44 5 3.0
2 47 7.0
22 25 1.0
23 44 3.0
46 38 1.0
3 16 2.0
3 53 1.0
3 48 1.0
10 2 2.0
3 30 2.0
23 3 1.0
3 14 1.0
23 49 1.0
46 61 1.0
46 49 1.0
25 58 1.0
31 32 1.0
46 54 1.0
60 29 2.0
58 25 1.0
24 31 1.0
3 47 1.0
46 50 1.0
38 30 1.0
46 63 2.0
25 31 1.0
46 29 3.0
8 29 2.0
23 60 1.0
46 53 1.0
3 11 1.0
54 5 1.0
10 60 2.0
58 41 2.0
46 39 1.0
3 54 1.0
25 44 3.0
23 10 3.0
31 45 1.0
31 44 1.0
46 7 1.0
31 24 2.0
31 46 2.0
3 37 1.0
41 57 1.0
63 60 3.0
46 58 2.0
58 2 3.0
46 55 1.0
29 35 1.0
3 52 1.0
46 47 1.0
60 41 1.0
10 3 6.0
46 40 1.0
25 10 3.0
2 23 4.0
60 63 1.0
8 35 1.0
3 57 1.0
31 59 1.0
47 23 1.0
3 35 1.0
16 29 2.0
60 13 1.0
5 22 1.0
31 2 4.0
10 5 1.0
2 24 1.0
31 36 1.0
63 3 1.0
58 23 1.0
2 31 2.0
58 46 2.0
38 25 1.0
3 55 1.0
6 5 1.0
54 60 2.0
3 39 1.0
28 23 2.0
46 21 1.0
5 44 3.0
31 18 1.0
41 7 2.0
31 47 3.0
8 2 2.0
41 58 3.0
31 42 1.0
3 28 2.0
7 31 2.0
3 27 1.0
38 23 1.0
46 43 1.0
23 57 2.0
41 44 5.0
24 21 1.0
3 24 2.0
7 44 1.0
47 10 1.0
47 57 1.0


thank you,
Simone