missjoaquim.com-TrustRank.pdf

TrustRank missjoaquim.com /pembicarainternetmarketingpakarseo/trustrank/ By abdurrachim.har

27/10/2016

TrustRank

TrustRank is an algorithm that can be used to bias PageRank by placing additional authority on human-reviewed, trusted sites. Trust propagates out from the trusted pages and sites to pages and sites they link to. TrustRank also can be used to neutralize the effects of some types of low-quality link building from untrusted sources as well as to flag high-PageRank, low-trust websites for human review.

In the TrustRank research paper, the seed sites fit the following criteria:

Seed sites linked to many other websites. DMOZ and the Yahoo! Directory, for example, were most likely seed sites. Seed sites were controlled by major corporations, educational bodies, or governmental bodies.

I believe TrustRank (or a similar algorithm) is a huge part of Google’s current search algorithm. If you look at a link through the eyes of a search engineer or search editor, rather than just looking at PageRank, you would do far better in terms of evaluating the value of a link.

Look at a site and ask yourself questions like, “Is this the type of site I would use as a seed site?” If the answer to that is “no,” then ask, “Is this site directly or indirectly associated with seed sites?” and “Why would quality seed sites want to link to this site or sites linking to this site?”

Topic Sensitive TrustRank

Since TrustRank is topic-independent, it could place too much relevancy on topics that are overrepresented in the seed set of trusted sites. Thus you could use DMOZ or the Yahoo! Directory to help extend out the seeded sites to a broader array of sites and topically bias their TrustRank score based on the categories in which they are listed. You could then filter out the bottom half of trusted sites in each category to prevent too many spam sites from being selected as trust sources.

1/3

There is significant effort being placed on looking for ways to move the PageRank model to a model based upon trust and local communities.

Link Spam Detection Based on Mass Estimation

TrustRank mainly works to give a net boost to good, trusted links. Link Spam Detection Based on Mass Estimation was a research paper aimed at killing the effectiveness of low-quality links. Essentially the thesis of this paper was that you could determine what percent of a site’s direct and indirect link popularity come from spammy locations and automate spam detection based on that.

The research paper is a bit complex, but many people have digested it. I posted on it at http://www.seobook.com/archives/001342.shtml.

Due to the high cost of producing quality information versus the profitability and scalability of spam, most pages on the web are spam. No matter what you do, if you run a quality website, you are going to have some spammy websites link to you and/or steal your content. Because my name is Aaron Wall, some idiots kept posting links to my sites on their “wall clock” spam site.

The best way to fight this off is not to spend lots of time worrying about spammy links, but to spend the extra time to build some links that could be trusted to offset the effects of spammy links.

Algorithms like the spam mass estimation research are going to be based on relative size. Since quality links typically have more PageRank (or authority by whatever measure they chose to use) than most spam links, you can probably get away with having 40 or 50 spammy links for every real, quality link.

Another interesting bit mentioned in the research paper was that generally the web follows power laws. This quote might be as clear as mud, so I will clarify it shortly.

A number of recent publications propose link spam detection methods. For instance, Fetterly et al. [Fetterly et al., 2004] analyze the indegree and outdegree distributions of web pages. Most web pages have in- and outdegrees that

2/3

follow a power- law distribution. Occasionally, however, search engines encounter substantially more pages with the exact same in- or outdegrees than what is predicted by the distribution formula. The authors find that the vast majority of such outliers are spam pages.

Indegrees and outdegrees above refer to link profiles, specifically to inbound links and outbound links. Most spam generator software and bad spam techniques leave obvious mathematical footprints.

If you are using widely hyped and marketed spam site generator software, most of it is likely going to be quickly discounted by link analysis algorithms since many

other people will be creating thousands of similar sites with similar link profiles and similar footprints.

3/3