That second part had a much longer explanation to it in the book. But as it is a little confusing, I\u2019ll give a really simplistic one here. <\/p>\n
It\u2019s important to understand the strengths of both citation and co-citation. <\/p>\n
In the first illustration, there are direct links \u2013 one page using a hyperlink to connect to another. But if page \u2018c\u2019 links to \u2018a\u2019 and \u2018b\u2019 and then page \u2018d\u2019 links to \u2018a\u2019 and \u2018b\u2019 and then page \u2018e\u2019 and so on and so on, what you can assume is that, although page \u2018a\u2019 and page \u2018b\u2019 do not directly hyperlink to each other, because they are co-cited so many times, there must be some connection between them.\u00a0<\/p>\n
What would a real-life example of that be?\u00a0<\/p>\n
Well, lists to begin with. Pages with the \u201ctop ten\u201d best-selling laptops, the \u201ctop ten\u201d sports personalities, or rock stars, you can see how co-citation is a big factor in those types of pages.\u00a0<\/p>\n
So where does this HITS algorithm that you may never have heard of come into play? <\/p>\n
There\u2019s a story that at the same time Page and Brin were working on their PageRank algorithm, Kleinberg was analyzing results at the top search engines of the day, including the fastest growing among them, Alta Vista. He thought that they were all pretty poor and produced very meager results in terms of how relevant they were to the query.\u00a0<\/p>\n
He searched for the term \u201cJapanese automotive manufacturer\u201d and was very unimpressed to note that none of the major names such as Toyota and Nissan appeared anywhere in the results, let alone where they should be at the top.<\/p>\n
After visiting the websites of the major manufacturers, he noticed one thing they all had in common: None of them had the words \u201cJapanese automotive manufacturer\u201d in the text on any site pages.\u00a0<\/p>\n
In fact, he searched for the term \u201csearch engine,\u201d and even Alta Vista didn\u2019t turn up in its own results for the very same reason. This caused him to start and focus on the connectivity of web pages to give a clue as to how relevant (and important) they were to a given query. <\/p>\n
So, he developed the HITS algorithm, which took the top thousand or more pages following a keyword search at Alta Vista and then ranked them according to their interconnectivity.\u00a0<\/p>\n
Effectively, he was using the link structure to form a network or \u201ccommunity\u201d around the keyword topic and, within that network, identify what he named \u201cHubs and Authorities.\u201d<\/p>\n
That\u2019s where the word \u201cauthority\u201d came into the SEO lexicon. The title of Kleinberg\u2019s thesis was \u201cAuthoritative Sources in a Hyperlinked Environment.\u201d\u00a0<\/p>\n
\u201cHub\u201d pages are those with many links connecting to \u201cauthorities\u201d on a given topic. The more hubs that link to a given authority, the more authority it gets. This is also mutually reinforcing. A good hub can also be a good authority and vice versa.<\/p>\n
As usual, no prizes for my graphic-creating skills all those years ago, but this is how I visualized it back in 2002. Hubs (red) are those that link out to many \u201cauthorities\u201d (blue) inside web communities.\u00a0<\/p>\n
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So, what\u2019s a \u201cweb community\u201d then? <\/p>\n
A web page data community refers to a set of web pages that has its own logical and semantic structures. <\/p>\n
The web page community considers each web page as a whole object rather than breaking down the web page into information pieces and reveals mutual relationships among the concerned web data. <\/p>\n
It is flexible in reflecting the web data nature, such as dynamics and heterogeneity. In the following graphic, each color represents a different community on the web. <\/p>\n
I\u2019ve always maintained that links attracted from within your own web community carry more prestige than those from outside your community.<\/p>\n
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I explained more about the importance of identifying communities this way 20 years ago:<\/p>\n
\n\u201cAnd as for linkage data: pages pointing (linking) to other pages can provide a massive amount of information about structure, communities, and hierarchy (largely referred to as the web\u2019s \u201ctopology\u201d). By using this methodology search engines can attempt to identify the intellectual structure (topology) and social networks (communities) of the web. However, there are many problems with scaling using methods of citation and co-citation analysis to deal with hundreds and hundreds of millions of documents with billions of citations (hyperlinks).
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