{ "id": 2003, "title": "Graph-based Features for Supervised Link Prediction", "url": "/competitions/FacebookRecruiting/discussion/2003", "author_name": "", "post_date": "2012-06-06T23:49:41.723Z", "votes": 27, "comment_count": 8, "views": 10266, "content": "

(alternate title: shameless self promotion...)

\r\n

Competitors may find this paper helpful.  Good luck to all!

", "messages": [ { "id": "11432", "postDate": "06/06/2012 23:49:41", "content": "

(alternate title: shameless self promotion...)

\r\n

Competitors may find this paper helpful.  Good luck to all!

", "rawMarkdown": "", "votes": null }, { "id": "11437", "postDate": "06/07/2012 07:21:18", "content": "

(Igor's message said that he was unable to download the file before he edited the message)

\r\n

Sorry for the issue. This is a problem that arose in the past, and may be a bug with Chrome, how Azure is serving the files, or our site.

\r\n

If you have trouble downloading the file, please right click and "Save File As ..." and it should download fine. Alternatively, different browsers should work as well.

", "rawMarkdown": "", "votes": null }, { "id": "11624", "postDate": "06/15/2012 02:44:21", "content": "

Interesting, but I wonder how to choose the negtive sample? as you know, all connections are positive sample.

", "rawMarkdown": "", "votes": null }, { "id": "11627", "postDate": "06/15/2012 18:38:29", "content": "

This is your opportunity to demonstrate your skills on a real-world social network dataset, and show them your creativity, open-mindedness and tenacity in the face of an open-ended predictive modeling problem! And this time without cheating...

", "rawMarkdown": "", "votes": null }, { "id": "11668", "postDate": "06/17/2012 02:13:01", "content": "

Sorry, Thanks guys, the same to you.

", "rawMarkdown": "", "votes": null }, { "id": "11892", "postDate": "06/26/2012 13:02:48", "content": "

Got a quick question about the EdgeRank algorithm on pg 1240 of the pdf (not sure if EdgeRank is relevant to this competition or not atm, just asking in general):

\r\n

Eqn 5:  Xn = (1-d)X0 + d(A+wA')Xn-1

\r\n

1.  (A+wA') as a whole must be row normalized?  I ran a few quick iterations in Matlab, and the probabilities exploded.  Some of them were wayyyy more than one.  Row normalization would be equivalent to a uniform probability of chosing each link?

\r\n

2.  I'm a little hazy on why this equation gives the probability of reaching whichever node in n steps.  

\r\n

I understand the (1-d)X0 chunk.  Am stuck on the other part which says d(A+wA')Xn-1.  If you multiply it out by hand slowly using 1st row as an example, I *think* it says something like "(probability of going from node 1 to node 2)*(probability of being\r\n at node 2) + (probability of going from node 1 to node 3)*(probability of being at node 3).......etc", and the entire result is supposed to be probability of being at node 1 after n steps??!!???  Why is this?

\r\n

If the entire (A+wA') matrix were transposed, so that it was (probability of going from node 2 to node 1)*(Prob at node 2)+(prob of going from node 3 to node 1)*(Prob at node 3)+....etc, that would be straightforward to understand.  If (A+wA') were symmetric,\r\n it wouldn't matter, but it doesn't have to be...

\r\n

 

\r\n

It's also *very* possible that I'm just suffering from lack of sleep..LOL.  

", "rawMarkdown": "", "votes": null }, { "id": "12277", "postDate": "07/10/2012 19:20:11", "content": "

 

\r\n

"The training data for the Random Forests was obtained by
\r\ncreating eight independent validation sets, each consisting of
\r\n4,000 data points (2,000 real edges and 2,000 fake edges).
\r\nThese validation sets were constructed to approximate the
\r\ndistributions of nodes and edges seen in the test set."

\r\n

 

\r\n

What method did you use?
\r\n
\r\n

", "rawMarkdown": "", "votes": null }, { "id": "12288", "postDate": "07/10/2012 23:57:36", "content": "

Snoopy, it's probably too late to reply to this but I didn't see it before. I also had a look at that paper and was puzzled by eq. 5. It is not correct (or at least, does not correspond with its description), since it does not yield a valid transition matrix.

\r\n

To fix it, (A+wA') should be column normalized. Alternatively, you could normalize A and A' and then combine them using weights w and (1-w). I cannot be sure, but I guess the authors meant one of those two options (which for them turn out to be\r\n equivalent, since they chose w = 0.5).

", "rawMarkdown": "", "votes": null }, { "id": "2478810", "postDate": "10/12/2023 08:13:13", "content": "

you can sample new edges and check if they are known already, if not they are likely a negative sample.

", "rawMarkdown": "you can sample new edges and check if they are known already, if not they are likely a negative sample.", "votes": null } ], "comments": [ { "id": 11437, "author_name": "benhamner", "author_url": "", "post_date": "06/07/2012 07:21:18", "content": "

(Igor's message said that he was unable to download the file before he edited the message)

\r\n

Sorry for the issue. This is a problem that arose in the past, and may be a bug with Chrome, how Azure is serving the files, or our site.

\r\n

If you have trouble downloading the file, please right click and "Save File As ..." and it should download fine. Alternatively, different browsers should work as well.

", "votes": null, "replies": [] }, { "id": 11624, "author_name": "helloworld6", "author_url": "", "post_date": "06/15/2012 02:44:21", "content": "

Interesting, but I wonder how to choose the negtive sample? as you know, all connections are positive sample.

", "votes": null, "replies": [ { "id": 2478810, "author_name": "oliverweissl", "author_url": "", "post_date": "10/12/2023 08:13:13", "content": "

you can sample new edges and check if they are known already, if not they are likely a negative sample.

", "votes": null, "replies": [] } ] }, { "id": 11627, "author_name": "leustagos", "author_url": "", "post_date": "06/15/2012 18:38:29", "content": "

This is your opportunity to demonstrate your skills on a real-world social network dataset, and show them your creativity, open-mindedness and tenacity in the face of an open-ended predictive modeling problem! And this time without cheating...

", "votes": null, "replies": [] }, { "id": 11668, "author_name": "helloworld6", "author_url": "", "post_date": "06/17/2012 02:13:01", "content": "

Sorry, Thanks guys, the same to you.

", "votes": null, "replies": [] }, { "id": 11892, "author_name": "snoopy0", "author_url": "", "post_date": "06/26/2012 13:02:48", "content": "

Got a quick question about the EdgeRank algorithm on pg 1240 of the pdf (not sure if EdgeRank is relevant to this competition or not atm, just asking in general):

\r\n

Eqn 5:  Xn = (1-d)X0 + d(A+wA')Xn-1

\r\n

1.  (A+wA') as a whole must be row normalized?  I ran a few quick iterations in Matlab, and the probabilities exploded.  Some of them were wayyyy more than one.  Row normalization would be equivalent to a uniform probability of chosing each link?

\r\n

2.  I'm a little hazy on why this equation gives the probability of reaching whichever node in n steps.  

\r\n

I understand the (1-d)X0 chunk.  Am stuck on the other part which says d(A+wA')Xn-1.  If you multiply it out by hand slowly using 1st row as an example, I *think* it says something like "(probability of going from node 1 to node 2)*(probability of being\r\n at node 2) + (probability of going from node 1 to node 3)*(probability of being at node 3).......etc", and the entire result is supposed to be probability of being at node 1 after n steps??!!???  Why is this?

\r\n

If the entire (A+wA') matrix were transposed, so that it was (probability of going from node 2 to node 1)*(Prob at node 2)+(prob of going from node 3 to node 1)*(Prob at node 3)+....etc, that would be straightforward to understand.  If (A+wA') were symmetric,\r\n it wouldn't matter, but it doesn't have to be...

\r\n

 

\r\n

It's also *very* possible that I'm just suffering from lack of sleep..LOL.  

", "votes": null, "replies": [] }, { "id": 12277, "author_name": "omarwugui", "author_url": "", "post_date": "07/10/2012 19:20:11", "content": "

 

\r\n

"The training data for the Random Forests was obtained by
\r\ncreating eight independent validation sets, each consisting of
\r\n4,000 data points (2,000 real edges and 2,000 fake edges).
\r\nThese validation sets were constructed to approximate the
\r\ndistributions of nodes and edges seen in the test set."

\r\n

 

\r\n

What method did you use?
\r\n
\r\n

", "votes": null, "replies": [] }, { "id": 12288, "author_name": "miguel2", "author_url": "", "post_date": "07/10/2012 23:57:36", "content": "

Snoopy, it's probably too late to reply to this but I didn't see it before. I also had a look at that paper and was puzzled by eq. 5. It is not correct (or at least, does not correspond with its description), since it does not yield a valid transition matrix.

\r\n

To fix it, (A+wA') should be column normalized. Alternatively, you could normalize A and A' and then combine them using weights w and (1-w). I cannot be sure, but I guess the authors meant one of those two options (which for them turn out to be\r\n equivalent, since they chose w = 0.5).

", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "11432": "", "11437": "", "11624": "", "11627": "", "11668": "", "11892": "", "12277": "", "12288": "", "2478810": "you can sample new edges and check if they are known already, if not they are likely a negative sample." }, "source": "meta" }