The web or the Internet contains a wide amount of information. Therefore, locating an appropriate portion of the information can be very challenging. This problem is compounded as the number of new users inexperienced at web searching and the amount of information on the web are growing rapidly.
For searching a corpus, a search engine improves the relevancy of the results by refining a standard relevancy score based on the inter-connectivity of the initially returned set of documents.
The search engine finds an initial set of relevant documents using other algorithms (like Trust Rank and PageRank), and then re-ranks search results based on the inter-connectivity of those documents. By matching the terms in the search query to a corpus of pre-stored web pages, the search engine accomplishes this.
In which a user is interested, search engines attempt to return that hyperlinks to web pages. Mostly, search engines base their determination of the user’s interest on search terms i.e. a search query which the user had entered. The aim of the search engine is to provide high quality links and relevant results to the user based on the search query.
From authoritative domains, if you have number of links, but still have a hard time ranking on search results then you might need to get links from many websites that includes in a “set” determined by this algorithm or we can say that you need links from sites that are at the top rank for your terms or the links that are much better than those of your competitors.
Your rankings may be recalculated, if you lack “community” exposure, and may be lowered in favor of sites that have more “community” links, even at the cost of some authority.
So, this is how the search engine rank search results by re-ranking the results based on Local Inter-Connectivity.