Timeline for Models of random graphs, for real computer networks
Current License: CC BY-SA 3.0
7 events
| when toggle format | what | by | license | comment | |
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| May 1, 2011 at 14:11 | comment | added | Raphael | Sure, I just wanted to point out that you can cover some global characteristics using that model. REG can cover some, too, but will fail to model inherently non-regular structures. (thanks, fixed) | |
| May 1, 2011 at 14:09 | history | edited | Raphael | CC BY-SA 3.0 |
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| May 1, 2011 at 10:06 | comment | added | Artem Kaznatcheev♦ | Thanks! I will take a closer look. Can't Hidden MDPs also capture properties like average degree? That seems like something a regular language should be able to capture, or am I confused? (Also, minor point: the Weinberg, Nebel link has a trailing character that kills the link, it is obvious what link you intended, but if you make more edits it might be worth fixing). | |
| Apr 30, 2011 at 10:01 | history | edited | Raphael | CC BY-SA 3.0 |
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| Apr 30, 2011 at 10:00 | comment | added | Raphael | Well, yea, non-context-free features get lost. But note that properties like (average) node degrees can be captured. For more, see my edit. | |
| Apr 30, 2011 at 7:20 | comment | added | Artem Kaznatcheev♦ | this is cool, but doesn't the context-free nature of SCFG force your learner to neglect certain global structure the networks in your training set might have? | |
| Apr 29, 2011 at 17:29 | history | answered | Raphael | CC BY-SA 3.0 |