thanks.That worked!
Increasing vertices and edges to higher values makes it
slow.(v=10000,e=20000) though.
Any optimization technique i can use?
Regards
Suvir



On Fri, Apr 26, 2013 at 4:22 AM, Gábor Csárdi <[email protected]> wrote:

> On Thu, Apr 25, 2013 at 6:08 PM, suvirbhargav <[email protected]> wrote:
>
>> i changed the code a bit by running neighbour opinion function in loop
>> and comparing the opinion "1" of net from past five time step .(assumption
>> is network will repeat itself in these five time step in steady state)
>> code: http://scsys.co.uk:8002/244444
>>
>> How do i change the opinion based not only on neighbour but also node
>> itself .
>> (as adopted opinion is majority opinion including opinion from both
>> neigbour and itself )
>>
>
> Then use the Opinion attribute of the 'x' vertex as well.
>
>
>> Also,cluster size is there in igrpah already there, but i want to
>> calculate largest cluster with opinion "1".
>>
>
> Delete all the vertices that are '2' with delete.vertices(net,
> V(g)[opinion==2]) and then call clusters().
>
> G.
>
>
>>
>> Thanks & Regards
>> Suvir
>>
>>
>>
>> On Thu, Apr 25, 2013 at 7:02 PM, Gábor Csárdi <[email protected]>wrote:
>>
>>> See ?sample and the prob argument. G.
>>>
>>>
>>> On Thu, Apr 25, 2013 at 12:54 PM, suvirbhargav <[email protected]>wrote:
>>>
>>>> thanks.
>>>>
>>>> In below ,How can i control the percentage of both 1 and 2,say 20% of 1
>>>> and rest 2?
>>>> V(net)$Opinion <- sample(1:2, vcount(net), replace=TRUE)
>>>> Sorry,this is R question though,may be
>>>>
>>>> Regards
>>>> Suvir
>>>>
>>>>
>>>> On Thu, Apr 25, 2013 at 4:37 PM, Gábor Csárdi <[email protected]>wrote:
>>>>
>>>>> On Thu, Apr 25, 2013 at 10:34 AM, Gábor Csárdi <[email protected]>wrote:
>>>>>
>>>>>> I see. Then what you could do is adding a vertex attribute, named
>>>>>> (say) "Opinion", and then update that based on the network structure, 
>>>>>> e.g.
>>>>>> (in R):
>>>>>>
>>>>>
>>>>> Sorry, sent it prematurely, this is the correct code:
>>>>>
>>>>> library(igraph)
>>>>> set.seed(42)
>>>>> net <- erdos.renyi.game(40, 2/40)
>>>>> V(net)$Opinion <- sample(1:2, vcount(net), replace=TRUE)
>>>>>
>>>>> coords <- layout.auto(net)
>>>>> colbar <- c("orange", "cyan")
>>>>> layout(rbind(1:2))
>>>>> par(mar=c(0,0,0,0))
>>>>> plot(net, vertex.color=colbar[V(net)$Opinion], layout=coords)
>>>>>
>>>>> ## Set the opinion based on the neighbors
>>>>> V(net)$Opinion <- sapply(V(net), function(x) {
>>>>>   if (degree(net, x)==0) {
>>>>>     V(net)$Opinion[x]
>>>>>   } else {
>>>>>     round(mean(V(net)$Opinion[neighbors(net,x)]))
>>>>>   }
>>>>> })
>>>>>
>>>>> plot(net, vertex.color=colbar[V(net)$Opinion], layout=coords)
>>>>>
>>>>> G.
>>>>>
>>>>> [...]
>>>>>
>>>>> --
>>>>> Gabor Csardi <[email protected]>     MTA KFKI RMKI
>>>>>
>>>>> _______________________________________________
>>>>> igraph-help mailing list
>>>>> [email protected]
>>>>> https://lists.nongnu.org/mailman/listinfo/igraph-help
>>>>>
>>>>>
>>>>
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>>>>
>>>
>>>
>>> --
>>> Gabor Csardi <[email protected]>     MTA KFKI RMKI
>>>
>>> _______________________________________________
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>>>
>>
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>
>
> --
> Gabor Csardi <[email protected]>     MTA KFKI RMKI
>
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