Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems

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Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems ( online-social-networks-measurement-analysis-and-applications )

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208 only index static HTML content, we considered only URLs of indexable content types that did not have any GET or POST parameters and ended in either .html or .htm. Further, we discarded URLs with an auto-refresh feature (such as the scoreboard sites for sports), as they would artificially bias the results against Google. This left us with 6,679 requests for 3,987 URLs. Our analysis shows that Google’s index covers only 62.5% of the requests, repre- senting 68.1% of the distinct URLs. This implies that about one third of all URLs requested by our users cannot be retrieved by searching Google! Our analysis also showed that the union of the PeerSpective peer indexes covers about 30.4% of the requested URLs. While PeerSpective achieves only half of the coverage of Google’s index, it does this with a much smaller size: at the end of the experiment, the Peer- Spective indexes contained 51,410 URLs, compared to Google’s index of over 8 billion URLs. Additionally, we found that 13.3% of the URLs viewed were contained in Peer- Spective but not in Google’s index. These documents were not available via Google’s search engine but had been requested before by someone in the peer network. This increase in coverage amounts to a 19.5% improvement by PeerSpective compared to normal Google search. It is worth noting that, for our small social network of com- puter science researchers, this improvement in coverage was possible by adding just a few thousand URLs to a Google index containing billions or URLs. Our results naturally raise the question, what are these documents that are of a of

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