CODEWITHSUNDEEP
r
Naveed Khan Wazir
Naveed Khan Wazir

Reputation: 195

how to make a network of user in a dataframe?

Hi I am new to r I have a problem i.e to find the network of user(uID) and network of articles(faID) from a data frame called w2 like

 faID      uID
  1        1256
  1        54789
  1        547821
  2        3258
  2        4521
  2        4528
  3        98745
  3        1256
  3        3258
  3        2145

this is just a example I have over 20000 articles what I want to make a relationship between users based on articles in a data frame format e.g.

**##for article one##**


1258  54789
1258  547821
47789 547821 

**##similarly for article 2##**

3258  4521
3258  4528
4528  4521

I was using the sparse matrix format but r memory do not allow me to find the network and centrality score of a user and article.any help would be highly appreciated.some of the other information are dput(head(w2,)) structure(list(faID = c(1L, 1L, 1L, 1L, 1L, 1L), uID = c(20909L,6661L, 1591L, 28065L,42783L, 3113L)), .Names = c("faID", "uID"), row.names=c(7L,9L,10L,12L,14L,16L), class =data.frame")

dim(w2) [1] 364323 2

Upvotes: 0

Views: 1389

Answers (1)

Beasterfield
Beasterfield

Reputation: 7113

Here is one answer (among many possible solutions) to the question how to construct a data.frame for the adjacencies

user -- (article) -- user

using dplyr:

library( dplyr )
edges <- tbl_df( tab ) %>% 
  group_by( article ) %>%
  do( {    
    tmp <- combn( sort(.$user),  m = 2 )
    data.frame( a = tmp[1,], b = tmp[2,], stringsAsFactors = FALSE )
  } ) %>%
  ungroup

which gives

Source: local data frame [12 x 3]

   article  a  b
1        1 u1 u2
2        1 u1 u3
3        1 u2 u3
4        2 u2 u4
...

If you want to summarise how many articles two users have in common you can do this by:

edges <- edges %>%
  group_by( a, b ) %>%
  summarise( article_in_common = length(article) ) %>%
  ungroup

Source: local data frame [6 x 3]

   a  b article_in_common
1 u1 u2                 1
2 u1 u3                 1
3 u1 u4                 1
4 u1 u6                 1
...

Note that this is possible, because you sorted the users prior to the call of combn.

From this data you can construct easily an igraph object:

library(igraph)
g <- graph.data.frame( select(edges, a, b, weight = article_in_common), directed = FALSE )

plot(g)

enter image description here

On this graph you cann call any kind of available centrality or community measures. See for instance ? centralize.scores.

Upvotes: 1

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