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Algorithms. Algorithms which were appearing for the first time in the cited articles. Such articles were connected by lines, so that, for example, one algorithm A appeared in article 1 and in article 2, this connection is depicted by line 1A2. In total, these connections gave rise to the lines of the graph.

As the data set grows, more and more lines have to be added. The web server has a limit on the number of requests it can serve concurrently, and we have to set a number of pages to load one by one.

For example, if we load a thousand pages in a single thread, then the graph will have to grow to a hundred and one thousand lines. For a web server, it means a lot of time for serving. Since we are searching for the right query before serving each page, we may need to make a hundred and one thousand guesses before finding a query that yields a fast page.

We have to solve this problem in a way that requires only a hundred lines.

We limit the number of lines to N, and for each line, we need N queries to find a page that can be served fast. In particular, we want the graph to have as many intersections as possible, and so we search for queries that yield a large number of lines in the graph.

We also search for links between pages that were found on different days. This could be a community on an external website.

In that case, we want to see as many lines as possible since it means that the community has a large number of members.

We also want to see as many pages as possible, since it means a large number of links between the pages. To end our search, we find the maximum number of hits that we can achieve in the time interval t given the server limit.

For a web server that can serve a maximum of N requests in parallel, the following query is optimal:

We don’t want to know the top N results, so we don’t want to search for the same query on the same day again. To do this, we write a Perl script and we generate


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