Reciprocal link exchange still is an important strategy of link popularity building despite all the measures taken by the search engines to diminish its effect. Back in 1999-2001 obtaining a quality link exchange was not difficult, and webmasters used to respond more willingly to an e-mail request. But as more people became aware of this strategy so the reciprocal linking scam started to be a common practice.

Sometimes I check my old ‘link exchange’ e-mail account I used to build link popularity for my very first website. There are lots of people contacting me daily with exchange proposals. Well, not actually people – they are mostly bots.

Probably one of the reasons I still maintain that e-mail is that those requests are a source of a persistent amusement for me. One example: a request in pink letters with images of dancing puppies and bouncing hearts written by a ‘blond chick’ (picture attached) asking me to link to her pharmacy site! Or maybe I just enjoy reading the admiring comments on the outlook and content of my site that precede every exchange proposal?

Link exchange scam is an interesting theme for a study per se and still awaits its researchers. But in the meanwhile the SEO community is being successful in summarizing the guidelines for the most perfect link exchange scam.

Interested? Read on!

Related Link: Nouveau Riche Scam Top ten RE scams

Link Popularity

Link building has always been a hot topic. In the beginning of the web hyperlinks were virtually the only way to get visitors to a site, because search engines were in their infancy. When search engines grew to be the major source of the web traffic, links didn’t lose their weight, as search algorithms started to rank sites according to the quantity and quality of their incoming links. And today links become increasingly important with the growing significance of the new Web 2.0 social networks. Interested? Read on!

Related Link: Link Building Company LinkBuildersPRO.com is one of the best companies at dramatically boosting your link popularity and increasing your natural search engine rankings.

Authority Threshold Algorithm (AT(k))

The idea behind AT(k) Algorithm is using only k highest authority weights instead of calculating average weight from every authority pointed by a hub. The parameter k is called authority threshold. A variant of an AT algorithm is MAX algorithm, where k=1, i.e. a hub is as good as the best authority it links to.

In general AT(k) algorithm uses the same formula as HITS. The difference is that when calculating the weight of a hub we consider top k authorities only, i.e. Fk(i) is a subset of outgoing links F(i). If the number of outgoing links |F(i)| is less or equal k than the AT(k) algorithm works exactly the same as HITS.

HUBAVG Algortihm

To overcome the shortcoming of the HITS algorithm of a hub getting a high weight when it points to numerous low-quality authorities, the following refinement was suggested. While using the same formula to calculate authority weights, the hub score h is now averaged by a number of outgoing links |F(i)|:

So in order to achieve a high weight a hub should link good authorities. Unfortunately this approach has its own flaw. Consider two hubs pointing to an equal number of equally good authorities. The two hubs are identical until one puts one more link to a low quality authority. The average sum of the authorities it points to sinks, and it gets penalized in weight. This is quite illogical but can be fixed by using so-called Authority Threshold Algorithm.

HITS Algorithm

This algorithm was first described by Jon Kleinberg in his work “Authoritative Sources in a Hyperlinked Environment” (1998). The idea behind the HITS (Hyperlink Induced Topic Distillation) algorithm is that the authorities and hubs mutually reinforce each other. Authority weight of a page is calculated as a sum of hub weights pointing to it, and weight of a hub – as a sum of weights of authorities pointed to by it. In other words a hub is as good as the authorities linked by it, and vice versa. Interested? Read on!

The link structure of the Web is highly sensitive to page topic. Pages tend to contain links pointing to other pages on the same broad topic, e.g. pages on investment banking often link to other business-related resources but rarely to sports portals. While using offline PageRank scores has an advantage of faster processing, it also creates a situation where some highly linked page receive higher ranking on topics for which they have no authority. A query-time adjustment of the scoring function is necessary to refine the search results. Some algorithms like HITS and Hilltop allow such an adjustment. However these algorithms have their own shortcomings that restrict their efficient use by search engines.

HITS algorithm calculates hubs and authorities in query-time but relies on a relatively small subset of the Web – the immediate neighborhood of a page, since otherwise computation time would be unacceptably long. Hilltop algorithm analyses a query and calculates score values by finding pages that seem to be experts in the query-specific topic. This algorithm restricts itself to popular queries, since it can’t produce score values when no experts for an uncommon search term are found.

Topic-Sensitive PageRank extends the original PageRank idea by adding a query-time topic-sensitive adjustment. Interested? Read on!

The PageRank Algorithm

PageRank extends the idea behind the InDegree algorithm by assigning different weights to the links. Links from high quality pages should make a stronger impact on the rank of a page. Therefore it is not only important how many incoming links a page has, but also how important the pointing pages are.

To determine the authority of internet pages PageRank simulates the behavior of a random web-surfer, Interested? Read on!

The InDegree Algorithm

This simplest algorithm uses page popularity as a ranking factor. Page popularity is measured as a number of incoming links – similar to document citation in the academic world. In the early days of the Web this algorithm was widely used by search engines. The InDegree algorithm is not very effective – we would need to consider links not just from any page but from those which are relevant to the query. Otherwise the algorithm can be easily manipulated by obtaining thousands of links from anywhere in the Web, thus artificially inflating the link popularity (link farms). The popularity a_i of a page i is calculated by a simple formula:

a_i = |B(i)|,

where B(i) is a set of pages pointing to page i, and |B(i)| is a number of elements in the set.

Reference

• Borodin, A, Roberts, G.O., Rosenthal, J.S. and Tsaparas, P. ‘Finding authorities and hubs from link structures on the World Wide Web’. In Proceedings of the 10 th International World Wide Web Conference, Hong Kong, May 2001. Available at http://citeseer.ist.psu.edu/borodin01finding.html

Relevance and Authority

When a user queries a search engine with a keyword, he expects more than just relevant results. For example if someone searches for ‘Bali vacations’ he would be disappointed to get a page of a personal blog with a story about John Doe’s awesome Bali vacation last summer. Obviously, what the user was looking for was a travel agent like Expedia. Thus it is critical that users get not just relevant but also authoritative results. And the more pages appear daily in the Internet the bigger is a shift from the relevance to the authoritativeness in search algorithms.

Nowadays the relevance of a page is defined differently. It is not just about the keyword saturation, or copy structure. Currently the context where the page exists defines its relevance. The context is a set of pages linking to or linked by the given page. If these pages are about Bali vacations, then it is naturally to expect a page linked by them to be about Bali vacations as well. The page content would be used to adjust the algorithm’s results in cases when links point to an irrelevant page, for example a widely used free web statistics system.

Link Analysis Ranking Algorithms

So how come the page content analysis is no longer enough to get relevant search results? There is a problem of abundance: the number of pages considered to be relevant basing on the page content analysis is too big for a human to digest. And this is where searching for authoritative pages helps to narrow down the results. But authority is even a vaguer notion than relevance. Authority has to express the importance and the weight of a web document. The nature of the Web as an interlinked hypertext environment suggests that links can be used to measure the degree of “public recognition” of web pages.

This idea has been existing since the creation of the Internet, and Jon Kleinberg was one of the first to create a workable approach, which he described in his seminal work “Authoritative Sources in a Hyperlinked Environment” (1998). He suggested that web pages can be either “hubs” or “authorities”. Authority is a page that has many incoming links or high in-degree. Authorities returned as relevant to some query should demonstrate an overlap in pages pointing to them. Those pages containing links to the relevant resources are called hubs. Hubs determine the relevance of authorities on a given topic and allow discarding other non-relevant pages with high in-degree.

Link analysis ranking algorithms use hyperlink graphs similar to the shown above. The nodes of the hyperlink graph are web pages, and links are the directed edges. The graph is simple: if there is more than one link between two nodes, only one is considered and neither are the links from a page to itself. A different weight can be assigned to edges (links) according to the web page analysis or other factors, which search engines consider important, e.g. link or domain age.

In my further posts I will describe the most widely used link analysis algorithms.

References

• Kleinberg, J. May 1997, ‘Authoritative sources in a hyperlinked environment’. Technical Report RJ 10076, IBM,. Available at http://citeseer.ist.psu.edu/article/kleinberg98authoritative.html
• Borodin, A, Roberts, G.O., Rosenthal, J.S. and Tsaparas, P. ‘Finding authorities and hubs from link structures on the World Wide Web’. In Proceedings of the 10 th International World Wide Web Conference, Hong Kong, May 2001. Available at http://citeseer.ist.psu.edu/borodin01finding.html

The idea behind the popularity ranking algorithms is that, by linking to a page, you imply that that page deserves attention. Search engines use links to determine the authority of pages in topics described by the link anchor text. The problem is that every link is considered as a positive endorsement with no regard to the real intention of the linking person. There is no effective way for a search engine to distinguish between positive and negative endorsements in links yet. Interested? Read on!