I am new here, so I might have missed a similar question. I am trying to do things good in my research and using proper statistical approaches, but in computer science, we had quite a weak training on statistical methods applied to our research.
So, what I am asking is probably trivial. Here is the problem: We have a dataset of del.icio.us (the social bookmarking website) and want to work on a subset of this dataset for a manual study of the usage of tags. In this dataset, we have urls, users and tags that are linked by triplets defining an association of a tag by a user to a url. We have 401 970 328 such triplets in our dataset, so we can't look at all of them and want to choose a sample of it.
Naively, at the beginning, we started with a purely random sampling to choose 500 user-bookmark pairs and all their associated tags. Because there are some more popular tags on del.icio.us, our sample has a long-tail distribution of tag usage, similar to the one from the whole dataset.
What I would like to check is that the actual distribution of each tag is similar in the sample as in the original dataset. That is, if "java" appears 10 times more often than "indonesia" in the dataset, it should be a similar distribution in the sample.
How would I go at doing this? one of the issue is that in the sample, some classes (tag) present in the full dataset will never appear.
The second question is also: how to decide of a good size for the sample?