How to create layout that will plot nodes in the same group close together?

Question

Using igraph, I aim to use community detection approaches, as i would like to draw a network layout that makes visible the distinct communities and their connections.

This is my code so far:

library(igraph)

dat=read.csv(file.choose(),header=TRUE) # choose an edgelist in .csv file format

I have a data frame with the following parameters;

  > > Var1 = node 1 Var2 = node 2 Value = edges (markov chain probabilities) > > 

    > head (dat, 100)
    >         Var1 Var2       value
    >     1      4    4 0.833333333
    >     2     10    4 0.000000000
    >     3     11    4 0.000000000
    >     4     12    4 0.000000000
    >     5     13    4 0.000000000
    >     6     21    4 0.000000000
    >     7     23    4 0.000000000
    >     8     31    4 0.000000000
    >     9     41    4 0.000000000
    >     10    42    4 0.000000000
    >     11    43    4 0.000000000
    >     12    44    4 0.000000000
    >     13    45    4 0.000000000
    >     14    46    4 0.000000000
    >     15    47    4 0.000000000
    >     16    48    4 0.000000000
    >     17    52    4 0.000000000
    >     18    53    4 0.000000000
    >     19    61    4 0.000000000
    >     20    62    4 0.000000000
    >     21    63    4 0.000000000
    >     22    71    4 0.000000000
    >     23    81    4 0.000000000
    >     24    82    4 0.000000000
    >     25    83    4 0.000000000
    >     26    91    4 0.000000000
    >     27    92    4 0.000000000
    >     28    93    4 0.000000000
    >     29   100    4 0.000000000
    >     30   111    4 0.000000000
    >     31     4   10 0.000000000
    >     32    10   10 0.000000000
    >     33    11   10 0.000000000
    >     34    12   10 0.010695187
    >     35    13   10 0.000000000
    >     36    21   10 0.000000000
    >     37    23   10 0.000000000
    >     38    31   10 0.000000000
    >     39    41   10 0.010869565
    >     40    42   10 0.000000000
    >     41    43   10 0.000000000
    >     42    44   10 0.000000000
    >     43    45   10 0.000000000
    >     44    46   10 0.000000000
    >     45    47   10 0.000000000
    >     46    48   10 0.000000000
    >     47    52   10 0.000000000
    >     48    53   10 0.000000000
    >     49    61   10 0.000000000
    >     50    62   10 0.074074074
    >     51    63   10 0.000000000
    >     52    71   10 0.000000000
    >     53    81   10 0.000000000
    >     54    82   10 0.000000000
    >     55    83   10 0.000000000
    >     56    91   10 0.000000000
    >     57    92   10 0.000000000
    >     58    93   10 0.000000000
    >     59   100   10 0.010526316
    >     60   111   10 0.018867925
    >     61     4   11 0.166666667
    >     62    10   11 0.000000000
    >     63    11   11 0.973409307
    >     64    12   11 0.010695187
    >     65    13   11 0.126126126
    >     66    21   11 0.000000000
    >     67    23   11 0.000000000
    >     68    31   11 0.000000000
    >     69    41   11 0.000000000
    >     70    42   11 0.008928571
    >     71    43   11 0.000000000
    >     72    44   11 0.038461538
    >     73    45   11 0.000000000
    >     74    46   11 0.000000000
    >     75    47   11 0.000000000
    >     76    48   11 0.000000000
    >     77    52   11 0.000000000
    >     78    53   11 0.000000000
    >     79    61   11 0.000000000
    >     80    62   11 0.000000000
    >     81    63   11 0.333333333
    >     82    71   11 0.000000000
    >     83    81   11 0.000000000
    >     84    82   11 0.000000000
    >     85    83   11 0.000000000
    >     86    91   11 0.071428571
    >     87    92   11 0.006622517
    >     88    93   11 0.000000000
    >     89   100   11 0.005263158
    >     90   111   11 0.018867925
    >     91     4   12 0.000000000
    >     92    10   12 0.000000000
    >     93    11   12 0.003798670
    >     94    12   12 0.673796791
    >     95    13   12 0.099099099
    >     96    21   12 0.000000000
    >     97    23   12 0.000000000
    >     98    31   12 0.029702970
    >     99    41   12 0.141304348
    >     100   42   12 0.017857143
    > 
    >     dim (dat)
    >     [1] 900   3








g = graph.data.frame(dat[,c('Var1','Var2')], directed = F)  # coerces the data into a two-column matrix format that igraph likes

cluster=cluster_walktrap(g)

list=groups (cluster)

g$value<-cluster$membership[as.character(g$Var1)]

g <- simplify(g) # remove loops and multiple edges



plot( cluster,g,  vertex.size = 5, edge.width = .1)

The output doesn't make any sense to me. Could you help me please? Thanks

Answer 1

Using a very minor change to your code, I get an understandable result (although I am only using the 100 points that you provided). Your code ran simplify after you ran cluster_walktrap . I ran simplify first. I also set the random seed so that the results would be reproducible. I get.

library(igraph)
g = graph.data.frame(dat[,c('Var1','Var2')], directed = F)  # coerces the data into a two-column matrix format that igraph likes
g <- simplify(g) # remove loops and multiple edges
cluster=cluster_walktrap(g)
list=groups (cluster)
g$value<-cluster$membership[as.character(g$Var1)]
set.seed(123)
plot(cluster,g,  vertex.size = 5, edge.width = .1)

This pretty clearly has two groups; blue nodes in one group, orange nodes in the other. Links that connect nodes in the same group are black. Those links that connect nodes in different groups are in red.