#47

In fact networks require just this sort of approach. Consider one of the most useful tools on the Internet – Google. Google searches the public domain and outputs a ranked list.

Word matches are only the starting point for ranking. The reason Google is different from Altavista and other earlier search engines is because it captures human input. It stores the results of what people select as useful and incorporates in the rankings. This is how Google describes PageRank in its IP documents>

Google describes PageRank:
PageRank relies on the uniquely democratic nature of the web by using its vast link structure as an indicator of an individual page’s value. In essence, Google interprets a link from page A to page B as a vote, by page A, for page B. But, Google looks at more than the sheer volume of votes, or links a page receives; it also analyzes the page that casts the vote. Votes cast by pages that are themselves “important” weigh more heavily and help to make other pages “important”.

Without accumulated human input, Google would not have its edge. Thus, the idea that you can engage in completely automated analysis is a false one, for now. Maybe in the future. But right now, we just don’t have enough analysts to put their ear against the firehose contemplated and assign them ranks, let alone correlate them to other events or intercepts that have been detected. In fact, trying to listen too broadly is a waste of resources in this context, because most of the persons of interests will be passing through super-nodes. Analytical resources are best used against those nodes, not browsing through the public chatter, unless they were looking for the super-nodes.

I maintain: not proved.