{"action":"created","issue":{"url":"https://api.github.com/repos/jhu-graphstat/LLG/issues/1","repository_url":"https://api.github.com/repos/jhu-graphstat/LLG","labels_url":"https://api.github.com/repos/jhu-graphstat/LLG/issues/1/labels{/name}","comments_url":"https://api.github.com/repos/jhu-graphstat/LLG/issues/1/comments","events_url":"https://api.github.com/repos/jhu-graphstat/LLG/issues/1/events","html_url":"https://github.com/jhu-graphstat/LLG/issues/1","id":169369539,"number":1,"title":"thoughts on current draft","user":{"login":"jovo","id":41842,"avatar_url":"https://avatars.githubusercontent.com/u/41842?v=3","gravatar_id":"","url":"https://api.github.com/users/jovo","html_url":"https://github.com/jovo","followers_url":"https://api.github.com/users/jovo/followers","following_url":"https://api.github.com/users/jovo/following{/other_user}","gists_url":"https://api.github.com/users/jovo/gists{/gist_id}","starred_url":"https://api.github.com/users/jovo/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/jovo/subscriptions","organizations_url":"https://api.github.com/users/jovo/orgs","repos_url":"https://api.github.com/users/jovo/repos","events_url":"https://api.github.com/users/jovo/events{/privacy}","received_events_url":"https://api.github.com/users/jovo/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":{"login":"dpmcsuss","id":547932,"avatar_url":"https://avatars.githubusercontent.com/u/547932?v=3","gravatar_id":"","url":"https://api.github.com/users/dpmcsuss","html_url":"https://github.com/dpmcsuss","followers_url":"https://api.github.com/users/dpmcsuss/followers","following_url":"https://api.github.com/users/dpmcsuss/following{/other_user}","gists_url":"https://api.github.com/users/dpmcsuss/gists{/gist_id}","starred_url":"https://api.github.com/users/dpmcsuss/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/dpmcsuss/subscriptions","organizations_url":"https://api.github.com/users/dpmcsuss/orgs","repos_url":"https://api.github.com/users/dpmcsuss/repos","events_url":"https://api.github.com/users/dpmcsuss/events{/privacy}","received_events_url":"https://api.github.com/users/dpmcsuss/received_events","type":"User","site_admin":false},"assignees":[{"login":"dpmcsuss","id":547932,"avatar_url":"https://avatars.githubusercontent.com/u/547932?v=3","gravatar_id":"","url":"https://api.github.com/users/dpmcsuss","html_url":"https://github.com/dpmcsuss","followers_url":"https://api.github.com/users/dpmcsuss/followers","following_url":"https://api.github.com/users/dpmcsuss/following{/other_user}","gists_url":"https://api.github.com/users/dpmcsuss/gists{/gist_id}","starred_url":"https://api.github.com/users/dpmcsuss/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/dpmcsuss/subscriptions","organizations_url":"https://api.github.com/users/dpmcsuss/orgs","repos_url":"https://api.github.com/users/dpmcsuss/repos","events_url":"https://api.github.com/users/dpmcsuss/events{/privacy}","received_events_url":"https://api.github.com/users/dpmcsuss/received_events","type":"User","site_admin":false},{"login":"TangRunze","id":5454829,"avatar_url":"https://avatars.githubusercontent.com/u/5454829?v=3","gravatar_id":"","url":"https://api.github.com/users/TangRunze","html_url":"https://github.com/TangRunze","followers_url":"https://api.github.com/users/TangRunze/followers","following_url":"https://api.github.com/users/TangRunze/following{/other_user}","gists_url":"https://api.github.com/users/TangRunze/gists{/gist_id}","starred_url":"https://api.github.com/users/TangRunze/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/TangRunze/subscriptions","organizations_url":"https://api.github.com/users/TangRunze/orgs","repos_url":"https://api.github.com/users/TangRunze/repos","events_url":"https://api.github.com/users/TangRunze/events{/privacy}","received_events_url":"https://api.github.com/users/TangRunze/received_events","type":"User","site_admin":false}],"milestone":null,"comments":7,"created_at":"2016-08-04T13:05:58Z","updated_at":"2016-08-07T18:01:49Z","closed_at":null,"body":"on the real data, we do a bunch of great analysis, but we skipped that on the theory/simulated data.\r\nhere are some related specific comments:\r\n\r\n- [x] in Fig 1, why not also add the 2 estimates (say for M=3) below, to make the point super clear early on?\r\n\r\n- [ ] in Fig 2, we only show scale relative efficiency, which does not provide a sense of what the overall error is.  perhaps another panel(s) showing the absolute error?  \r\n\r\n- [x] in Fig 2 & 3, clarify more explicitly which lines are estimated from data, and which are theoretical.  \r\n\r\n- [x] i'm a bit confused about the ordering of the figs/results.  we first talk about theory, then fig 2 is about simulations, then fig 3 is about theory again? let's consolidate theory, and then simulations, and then real data?\r\n\r\n- [x] Fig 3 shows theoretical results, do we have numerical results that demonstrate that we are doing well in those settings? \r\n\r\n- [ ] Fig 5 & 6 are great and super illustrative! why not also show them much earlier (like after fig 1) for the SBM? same question for fig 4 (choosing one \"atlas = # of blocks\").\r\n\r\n- [x] in general, i think the text could do a better job explaining why the numerical simulations are important.  it is not only to demonstrate that our estimators work.  we also get a lot more information, say, about finite sample efficiency. in other words, numerical experiments do not merely \"illustrate\" theoretical results, they do much more.\r\n\r\n- [x] in the text, we kind of pretend that ASE always works better on the real data. in truth, it only works better when sample size is extremely low.  we should address this in 2 ways.\r\nfirst, in the discussion, we can talk about typical sample sizes, and the desire to stratify clinical populations on the basis of various phenomological covariates, like age, gender, and even family history.  \r\nsecond, even though our estimators don't have better MSE, they are better in other ways.  in particular, the low-rank approximations yield better interpretability, enable us to more easily identity vertices that are interesting, etc. \r\nwe probably want a sentence about each of those in the results, and then a few more in the discussion.\r\n"},"comment":{"url":"https://api.github.com/repos/jhu-graphstat/LLG/issues/comments/238097778","html_url":"https://github.com/jhu-graphstat/LLG/issues/1#issuecomment-238097778","issue_url":"https://api.github.com/repos/jhu-graphstat/LLG/issues/1","id":238097778,"user":{"login":"jovo","id":41842,"avatar_url":"https://avatars.githubusercontent.com/u/41842?v=3","gravatar_id":"","url":"https://api.github.com/users/jovo","html_url":"https://github.com/jovo","followers_url":"https://api.github.com/users/jovo/followers","following_url":"https://api.github.com/users/jovo/following{/other_user}","gists_url":"https://api.github.com/users/jovo/gists{/gist_id}","starred_url":"https://api.github.com/users/jovo/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/jovo/subscriptions","organizations_url":"https://api.github.com/users/jovo/orgs","repos_url":"https://api.github.com/users/jovo/repos","events_url":"https://api.github.com/users/jovo/events{/privacy}","received_events_url":"https://api.github.com/users/jovo/received_events","type":"User","site_admin":false},"created_at":"2016-08-07T18:01:49Z","updated_at":"2016-08-07T18:01:49Z","body":"- [ ] i don't understand the sentence \"To illustrate Theorem 3.2, Fig 2 shows 1/ρs + 1/ρt, the scaled asymptotic RE, for 207 each s,t ∈ {1,2} as ρ1 changes from 0.01 to 0.50 for a two block model.\"  if we have a 2-block model, why don't we just name the parameters rho_1 & rho_2? what does adding rho_s & rho_t give us? it seems unnecessary notaiton.\r\n- [ ] rho is only introduced in the theory section, but it is a parameter of the (unconditional) SBM, so let's introduce it there?\r\n- [ ] scaled RE is an important concept.  it should be introduced in methods, and explained why we use it instead of absolute RE.  it should also be very carefully defined (it does not seem to be now).\r\n- [ ] in fig 2, should we draw a line at scaled RE = 4? \r\n- [ ] fig 1 is now great, the fact that our estimator is WAY better than MSE *pops out*.  that is what we want each figure to do.  once we (internally) know the main point of the figure, we draw it so that it pops out.  in fig 2, what is the single main take-away? it should be immediately obvious. i'm not sure what it is even after reading the caption. in other words: are we always winning? if not, when are we winning, when losing, when tie?\r\n- [ ] \"To illustrate Theorem 3.2, Fig 2 shows 1/ρs + 1/ρt, the scaled asymptotic RE, for 207 each s,t ∈ {1,2} as ρ1 changes from 0.01 to 0.50 for a two block model. \"  the parameters of the block model have not yet been provided, they should be.  it should be stated that they don't matter, so the figure would look the same for any parameters (is that true?).\r\n- [ ] \"While there is no compact analytical formula for the relative 214 efficiency of Pˆ versus A ̄ in the general RDPG case, using the same ideas as in 215 Theorem 3.2, we can show that RE(A ̄ij , Pˆij ) = O(1/N ).\" is that a lemma? a conjecture? a theorem? clarify.\r\n- [ ] \"The reason for this is because for larger M, more of the eigenvectors of A ̄ will 221 begin to concentrate around the eigenvectors of the mean graph. \"  i wouldn't say \"mean graph\" here, i don't think, maybe expectation of the distribution?\r\n- [ ] \" optimal embedding dimension will increase,\"  optimal with regard to what? estimating the mean? clarify.\r\n- [ ] \"As a result, RE(A ̄,Pˆ) will 224 increase as M increases for full-rank models.\"  well, it will converge to 1, right? we should clarify.\r\n- [ ] \r\n\r\n\r\n"}}
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