Talk:Random graph

message: I corrected the statement the models are interchangable for M \simeq pn. The cited source states capital N instead of the number of vertices n in this formula, previously introduced as the maximal possible amount of edges. Also everything else makes no sense, because the decision to add the edge or not is made "the number of possible edges" times and not "number of vertices" times, which would much less...  — Preceding unsigned comment added by 131.111.7.91 (talk) 23:05, 7 April 2013 (UTC)
thread_title:  Equivalence of G(n,p) and G(n,M) 

message: And why is there a "blah blah blah etc." ? Is this a case of vandalism ? I'm writing this on 29-4-2006. I'll wait for a couple of months and if the percolation part has not been explained by then, I'll erase it.
29-6-2006: I'm glad to see that the "blah blah" part has been erased but the reference to percolation has not been explained. I've read the article on "percolation" and although some things mentioned there do have a graph structure no connection is mentioned or is obvious specifically with random graphs. A lot of things have a graph structure and there's no more reason for percolation to be there than for dozens of other things. So I'm erasing the reference to it. I'll check again in the future to see if anyone objects.
04-11-2006: Percolation indeed has to do with random graphs. Suppose the graph grows with small-sized edges in something solid. What's the probability of any vertex touching the other side of the solid wall? That's percolation. And that's also r.g.62.118.128.63 01:12, 4 November 2006 (UTC)
thread_title:  What does percolation have to do with random graphs ? 

message: Is the Erdos-Renyi model related to random graphs? It would be great if the Erdos-Renyi model article would be created, as there are a bunch of articles which link to it. Oleg Alexandrov (talk) 16:45, 28 January 2007 (UTC)
thread_title:  Erdos-Renyi model 

message: What this page really needs are some visualizations of random graphs versus other more organized graphs.  You can see the difference when you look at them. Pwoolf (talk) 19:18, 8 May 2008 (UTC)
thread_title:  visualizations 

message: The text says that an Erdos-Renyi graph is a snapshot of the "random graph process" at a particular time.    It seems to me that it is a snapshot at a (binomially-distributed?) random time.  Is that correct? LachlanA (talk) 21:15, 13 June 2008 (UTC)
thread_title:  Snapshots 

message: In the language of probability it is customary to call "random object X of the set S" any measurable map X:\Omega\to S, from a base probability space \textstyle (\Omega, \mathcal F, P) to S (once a sigma algebra has been fixed in S).
According to this, I would expect that "random graph", say with (possibly infinite) vertex set V, in the most general meaning should refer to any measurable map X:\Omega\to 2^K, where K is the set of all unordered pairs of V, and of course, the power set 2^K is equipped with the product sigma algebra. In other words, in this wider definition any probability distribution of edges is allowed, not assuming independency of edges, nor that they are i.d., like e.g. in the Erdos-Renyi model. I am not a probabilist, and I wonder if random graphs are studied in this generality...Thanks. PMajer79.38.22.37 (talk) 17:47, 16 October 2008 (UTC)
thread_title: Other models?

message: #REDIRECT Strike-through text
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--116.71.49.85 (talk) 09:21, 18 August 2009 (UTC)
thread_title:  Ameen Nawaz Khan

message: I do not understand how the section "Interdependent Graphs" fits on the "Random graph" page. Why this is relevant to random graphs? 128.194.239.166 (talk) 21:44, 26 November 2012 (UTC)
replies: text: I took it out.  If someone comes here with a good argument of why it is relevant, we can reconsider. McKay (talk) 05:39, 6 May 2013 (UTC)
thread_title:  Why interdependent graphs? 

message: Substituted at 20:12, 1 May 2016 (UTC)
thread_title: Assessment comment