If this data were real then going deeper than one or two nodes would probably be useless, friends of friends of friends probably don't have much to tell you about potential friends. However, with so few connections associated with each node this becomes\r\n necessary to build up a list of 10 possible friends.
\r\nAlso, facebook data is undirected (friendship is always recipricol), however this data is not.
\r\nThe competition seems a little boring with data that is so extremely unrealistic. I lost interest.
", "messages": [ { "id": "11445", "postDate": "06/07/2012 17:04:57", "content": "If this data were real then going deeper than one or two nodes would probably be useless, friends of friends of friends probably don't have much to tell you about potential friends. However, with so few connections associated with each node this becomes\r\n necessary to build up a list of 10 possible friends.
\r\nAlso, facebook data is undirected (friendship is always recipricol), however this data is not.
\r\nThe competition seems a little boring with data that is so extremely unrealistic. I lost interest.
", "rawMarkdown": "", "votes": null }, { "id": "11450", "postDate": "06/07/2012 20:15:57", "content": "My guess is Intstagram. ;-)
", "rawMarkdown": "", "votes": null }, { "id": "11454", "postDate": "06/08/2012 02:41:08", "content": "Instagram or something similar makes sense.
\r\nIn fact, although Facebook friend links are symmetric, following is not, since you can block someone from showing up on your newsfeed but still be friends with them.
\r\nThe graph might also be randomly generated. Remember that this is a test and they are looking for people who know a lot about social networks, graph theory, and AI.
", "rawMarkdown": "", "votes": null }, { "id": "11469", "postDate": "06/08/2012 13:54:00", "content": "I can suppose that dual direction links make sense in Facebook. (A) makes a friend request to (B) so this is an outgoing link. If (B) accept it then B->A link will be created, the incoming link. So if A has a friend request from B which are the criteria\r\n for the outgoing link to be created? I read in another thread that algorithms that simply create links based on missing dual edges but incoming edge exists, choosing a random edge from this set get an evaluation near 0.6. What could improve this??? Network\r\n structure possibly and node properties does matter because nodes with a degree from 0 to average tend to create links to nodes with similar degree (assortative) but after that users with very high degree tend to create links with nodes of a much lower degree\r\n (disassortative firstly but quite like random at higher degrees). This is a fact in Facebook relationships as FB celebritites accept friends without any reason but normal users accept requests only from users they or their friends know.\r\n
", "rawMarkdown": "", "votes": null }, { "id": "11493", "postDate": "06/09/2012 06:43:17", "content": "There doesn't seem to be any prize for relating this data to a natural network. Real shame, but that's that...
\r\nI suppse that if this were Faceook, the action in question looks less like making friends, then the spread of third party content. 'Guess this puts us in an advertieser's shoes, especially one of the kind that does not do well with content search.
\r\nIn any case, the most interesting piece of information in the formulation of the problem here is the source, so far [attempted humor & rule of link sorting] ...
", "rawMarkdown": "", "votes": null }, { "id": "11780", "postDate": "06/21/2012 02:51:17", "content": "I *very much* doubt the graph was generated artificially. Lately -- in my work life -- I've been looking at various metrics for social graph (see Stanford's SNAP collection), and graphs generated using RMAT (google graph500) and preferential attachement.\r\n The generated graphs do not begin to approach the complexity (community structure) of real-world graphs. Since it seems intuitive that predicting who X will follow is all based on community structure ... as I said, I doubt the graph was artificially generated.
", "rawMarkdown": "", "votes": null }, { "id": "11969", "postDate": "06/27/2012 23:36:27", "content": "Instagram makes sense...
", "rawMarkdown": "", "votes": null } ], "comments": [ { "id": 11450, "author_name": "yarreg", "author_url": "", "post_date": "06/07/2012 20:15:57", "content": "My guess is Intstagram. ;-)
", "votes": null, "replies": [] }, { "id": 11454, "author_name": "jamespaton", "author_url": "", "post_date": "06/08/2012 02:41:08", "content": "Instagram or something similar makes sense.
\r\nIn fact, although Facebook friend links are symmetric, following is not, since you can block someone from showing up on your newsfeed but still be friends with them.
\r\nThe graph might also be randomly generated. Remember that this is a test and they are looking for people who know a lot about social networks, graph theory, and AI.
", "votes": null, "replies": [] }, { "id": 11469, "author_name": "sotos44906", "author_url": "", "post_date": "06/08/2012 13:54:00", "content": "I can suppose that dual direction links make sense in Facebook. (A) makes a friend request to (B) so this is an outgoing link. If (B) accept it then B->A link will be created, the incoming link. So if A has a friend request from B which are the criteria\r\n for the outgoing link to be created? I read in another thread that algorithms that simply create links based on missing dual edges but incoming edge exists, choosing a random edge from this set get an evaluation near 0.6. What could improve this??? Network\r\n structure possibly and node properties does matter because nodes with a degree from 0 to average tend to create links to nodes with similar degree (assortative) but after that users with very high degree tend to create links with nodes of a much lower degree\r\n (disassortative firstly but quite like random at higher degrees). This is a fact in Facebook relationships as FB celebritites accept friends without any reason but normal users accept requests only from users they or their friends know.\r\n
", "votes": null, "replies": [] }, { "id": 11493, "author_name": "anav45844", "author_url": "", "post_date": "06/09/2012 06:43:17", "content": "There doesn't seem to be any prize for relating this data to a natural network. Real shame, but that's that...
\r\nI suppse that if this were Faceook, the action in question looks less like making friends, then the spread of third party content. 'Guess this puts us in an advertieser's shoes, especially one of the kind that does not do well with content search.
\r\nIn any case, the most interesting piece of information in the formulation of the problem here is the source, so far [attempted humor & rule of link sorting] ...
", "votes": null, "replies": [] }, { "id": 11780, "author_name": "davidhysom", "author_url": "", "post_date": "06/21/2012 02:51:17", "content": "I *very much* doubt the graph was generated artificially. Lately -- in my work life -- I've been looking at various metrics for social graph (see Stanford's SNAP collection), and graphs generated using RMAT (google graph500) and preferential attachement.\r\n The generated graphs do not begin to approach the complexity (community structure) of real-world graphs. Since it seems intuitive that predicting who X will follow is all based on community structure ... as I said, I doubt the graph was artificially generated.
", "votes": null, "replies": [] }, { "id": 11969, "author_name": "nikoskavadias", "author_url": "", "post_date": "06/27/2012 23:36:27", "content": "Instagram makes sense...
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "11445": "", "11450": "", "11454": "", "11469": "", "11493": "", "11780": "", "11969": "" }, "source": "meta" }