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Modification of the PageRank Algorithm

To implement additional factors in PageRank, the original PageRank algorithm has again to be modified. Since we have to assume that PageRank calculations are still based on numerous iterations and for the purpose of short computation times, we have to consider to keep the number of database queries during the iterations as small as possible. Therefore, the following modification of the PageRank algorithm shall be assumed:

PR(A) = (1-d) + d (PR(T1)×L(T1,A) + ... + PR(Tn)×L(Tn,A))

Here, L(Ti,A) represents the evaluation of a link which points from page Ti to page A. L(Ti,A) withal replaces the PageRank weighting of page Ti by the number of outbound links on page Ti which was given by 1/C(Ti). L(Ti,A) may consist of several factors, each of them having to be determined only once and then being merged to one value before the iterative PageRank calculation begins. So, the number of database queries during the iterations stays the same, although, admittedly, a much larger database has to be queried at each step in comparison to the computation by use of the original algorithm, since now there is an evaluation of each link instead of an evaluation of pages (by the number of their outbound links).

Different Evaluation of Links within a Document
Two of the criteria for the evaluation of links mentioned by Lawrence Page in his PageRank patent specifications are the visibilty of a link and its position within a document. Regarding the Random Surfer Model, those criteria reflect the probability for the random surfer clicking on a link on a specific web page. In the original PageRank algorithm, this probability is given by the term (1/C(Ti)), whereby the probability is equal for each link on one page.

Assigning different probabilities to each link on a page can,
for instance, be realized as follows:

We take a look at a web consisting of three pages A, B anc C, where each of these pages has outbound links to both of the other pages. Links are weighted by two evaluation criteria X and Y. X represents the visibility of a link. X equals 1 if a link is not particularly emphasized, and 2 if the link is, for instance, bold or italic. Y represents the position of a link within a document. Y equals 1 if the link is on the lower half of the page, and 3 if the link is on the upper half of the page. If we assume a multiplicative correlation between X and Y, the links in our example are evaluated as follows:

X(A,B) × Y(A,B) = 1 × 3 = 3
X(A,C) × Y(A,C) = 1 × 1 = 1
X(B,A) × Y(B,A) = 2 × 3 = 6
X(B,C) × Y(B,C) = 2 × 1 = 2
X(C,A) × Y(C,A) = 2 × 3 = 6
X(C,B) × Y(C,B) = 2 × 1 = 2

For the purpose of determinig the single factors L, the evaluated links must not simply be weighted by the number of outbound links on one page, but in fact by the total of evaluated links on the page. Thereby, we get the following weighting quotients Z(Ti) for the single pages Ti:

Z(A) = X(A,B) × Y(A,B) + X(A,C) × Y(A,C) = 4
Z(B) = X(B,A) × Y(B,A) + X(B,C) × Y(B,C) = 8
Z(C) = X(C,A) × Y(C,A) + X(C,B) × Y(C,B) = 8

The evaluation factors L(T1,T2) for a link which is pointing from page T1 to page T2 are hence given by

L(T1,T2) = X(T1,T2) × Y(T1,T2) / Z(T1)

Their values regarding our example are as follows:

L(A,B) = 0.75
L(A,C) = 0.25
L(B,A) = 0.75
L(B,C) = 0.25
L(C,A) = 0.75
L(C,B) = 0.25

At a damping factor d of 0.5, we get the following equations for the calculation of PageRank values:

PR(A) = 0.5 + 0.5 (0.75 PR(B) + 0.75 PR(C))
PR(B) = 0.5 + 0.5 (0.75 PR(A) + 0.25 PR(C))
PR(C) = 0.5 + 0.5 (0.25 PR(A) + 0.25 PR(B))

Solving these equations gives us the follwing PageRank values for our example:

PR(A) = 819/693
PR(B) = 721/693
PR(C) = 539/693

First of all, we see that page A has the highest PageRank of all three pages. This is caused by page A receiving the relatively higher evaluated link from page B as well as from page C.

Furthermore, we see that even by the evaluation of single links, the sum of the PageRank values of all pages equals 3 (2079/693) and thereby the total number of pages. So, the PageRank values computed by our modified PageRank algorithm can be used for the general ranking of web pages by Google without any normalisation being needed.

Different Evaluation of Links by Page Specific Criteria

Besides the unequal evaluation of links within a document, Lawrence Page mentions the possibility of evaluating links according to criteria which are based upon the linking page. At first glance, this does not seem necessary since it is the main principle of PageRank to rank pages the higher, the more high ranking pages link to them. But, at the time of their scientific work on PageRank, Page and Brin have already recognized that their algorithm is vulnerable to artificial inflation of PageRank.

An artificial influence on PageRank might be exerted by webmasters who generate a multitude of web pages whose links distribute PageRank in a way that single pages within that system receive a special importance. Those pages can have a high PageRank without being linked to from other pages with high PageRank. So, not only the concept of PageRank is undermined, but also the search engine's index is spammed with an innumerable amount of web pages which were solely created to influence PageRank.

In his patent specifications for PageRank, Lawrence Page presents the evaluation of links by the distance between pages as a means to avoid the artificial inflation of PageRank, because the bigger the distance between two pages, the less likely has one webmaster control over both. A criterium for the distance between two pages may be if they are on the same domain or not. In this way, internal links would be weighted less than external links. In the end, any general measure of the distance between links can be used to determine such a weighting. This comprehends if pages are on the same server or not and also the geographical distance between servers.

As another indicator for the importance of a document, Lawrence Page mentions the up-to-dateness of the documents which link to it. This argument considers that the information on a page is less likely outdated, the more pages which have been modified recently link to it. In contrast, the original PageRank concept, just like any method of measuring link popularity, favours older documents which gained their inbound links in the course of their existence and have at a higher probability been modified less recently than new documents. Basically, recently modified documents may be given a higher evaluation by weighting the factor (1-d). In this way, both those recently modified documents and the pages they link to receive a higher PageRank. But, if a page has been modified recently, is not necessarily an indicator for the importance of the information presented on it. So, as suggested by Lawrence Page, it is advisable not to favour recently modified pages but only their outbound links.

Finally, Page mentions the importance of the web location of a page as an indicator of the importance of its outbound links. As an example for an important web location he names the root page of a domain, but, in the end, Google could exert influence on PageRank absolutely arbitrarily.

To implement the evaluation of the linking page into PageRank, the evaluation factor of the modified algorithm must consist of several single factors. For a link that points from page Ti to page A, it can be given as follows:

L(Ti,A) = K(Ti,A) × K1(Ti) × ... × Km(Ti)

where K(Ti,A) is the above presented weighting of a single link within a page by its visibility or position. Additionally, an evaluation of page Ti by m criteria which are represented by the factors Kj(Ti) takes place.

To implement the evaluation of the linking pages, not only the algorithm but also the proceedings of PageRank calculation have to be modified. This shall be illustrated by an example.

We take a look at a web consisting of three pages A, B and C, whereby page A links to the pages B and C, page B links to page C and page C links to page A. The outbound links of one page are evaluated equally, so there is no weighting by visibilty or position. But now, the pages are evaluated by one criterium. In this way, an inbound link from page C shall be considered four times as important as an inbound link from one of the other pages. After weighting by the number of pages, we get the following evaluation factors:

K(A) = 0.5
K(B) = 0.5
K(C) = 2

At a damping factor d of 0.5, the equations for the computation of the PageRank values are given by

PR(A) = 0.5 + 0.5 × 2 PR(C)
PR(B) = 0.5 + 0.5 × 0.5 × 0.5 PR(A)
PR(C) = 0.5 + 0.5 (0.5 PR(B) + 0.5 × 0.5 PR(A))

Solving the equations gives us the follwing PageRank values:

PR(A) = 4/3
PR(B) = 2/3
PR(C) = 5/6

At the current modifications of the PageRank algorithm, the accumulated PageRank of all pages no longer equals the number of pages. The reason therefore is that the weighting of the page evaluation by the number of pages was not appropriate. To determine the proper weighting, the web's linking structure would have to be anticipated, which is not possible in case of the actual WWW. Therefore, the PageRank calculated by an evaluation of linking pages has to be normalized if there shall not be any unfounded effects on the general ranking of pages by Google. Within the iterative calculation, a normalization would have to take place after each iteration to minimize unintentional distortions.

In the case of a small web, the evaluation of pages often causes severe distortions. In the case of the actual WWW, these distortions should normally equalise by the number of pages. Indeed, it is to be expected that the evaluation of the distance between pages will cause distortions on PageRank, since pages with many inbound links surely tend to be linked to from different geographical regions. But such effects can be anticipated by experience from previous calculation periods, so that a normalisation would only have to be marginal.

In either case, implementing additional factors in PageRank is possible. Indeed, the computation of PageRank values would take more time

Theme-based PageRank :

For many years now, the topic- or theme-based homogeneity of websites has been dicussed as a possible ranking criterion of search engines. There are various theoretical approaches for the implementation of themes in search engine algorithms which all have in common that web pages are no longer ranked only based on their own content, but also based on the content of other web pages. For example, the content of all pages of one website may take influence on the ranking of one single page of this website. On the other hand, it is also conceivable that one page's ranking is based on the content of those pages which link to it or which it links to itself.

The potential implementation of a theme-based ranking in the Google search engine is discussed controversially. In search engine optimization forums and on websites on this topic we can over and over again find advice that inbound links from sites with a similar theme to our own have a larger influence on PageRank than links from unrelated sites. This hypothesis shall be discussed here. Therefore, we first of all take a look at two relatively new approaches for the integration of themes in the PageRank technique: on the one hand the "intelligent surfer" by Matthew Richardson and Pedro Domingos and on the other hand the Topic-Sensitive PageRank by Taher Haveliwala. Subsequently, we take a look at the possibility of using content analyses in order to compare the text of web pages, which can be a basis for weighting links within the PageRank technique.

The "Intelligent Surfer" by Richardson and Domingos
Matthew Richardson and Pedro Domingos resort to the Random Surfer Model in order to explain their approach for the implementation of themes in the PageRank technique. Instead of a surfer who follws links completely at random, they suggest a more intelligent surfer who, on the one hand, only follows links which are related to an original search query and, on the other hand, also after "getting bored" only jumps to a page which relates to the original query.

So, to Richardson and Domingos' "intelligent surfer" only pages are relevant that contain the search term of an initial query. But since the Random Surfer Model does nothing but illustrate the PageRank technique, the question is how an "intelligent" behaviour of the Random Surfer influences PageRank. The answer is that for every term occuring on the web a separate PageRank calculation has to be conducted and each calculation is solely based on links between pages which contain that term.

Computing PageRank this way causes some problems. They especially appear for search terms that do not occur so often on the web. To make it into the PageRank calculations for a specific search term, that term has not only to appear on someone's page, but also on the pages that link to it. So, the search results would often be based on small subsets of the web and may omit relevant sites. In addition, using such small subsets of the web, the algorithms are more vulnerable to spam by automatically generating numerous pages.

Additionally, there are serious problems regarding scalability. Richardson and Domingos estimate the memory and computing time requirements for several 100,000 terms 100-200 times higher compared to the original PageRank calculations. Regarding the large number of small subsets of the web, these numbers appear to be realistic.

The higher memory requirements should not be so much of a problem because Richardson and Domingos correctly state that the term specific PageRank values constitute only a fraction of the data volume of Google's inverse index. However, the computing time requirements are indeed a large problem. If we assume just five hours for a conventional PageRank calculation, then this would last about 3 weeks based on Richardson and Domingos' model, which makes it unsuitable for actual employment.

Taher Haveliwala's Topic-Sensitive PageRank

The approach of Taher Havilewala seems to be more practical for actual employment. Just like Richardson and Domingos, also Havilewala suggests the computation of different PageRanks for different topics. However, the Topic-Sensitive PageRank does not consist of hundreds of thousands of PageRanks for different terms, but of a few PageRanks for different topics. Topic-Sensitive PageRank is based on the link structure of the whole web, whereby the topic sensitivity implies that there is a different weighting for each topic.

The basic principle of Haveliwala's approach has already been described in our section on the "Yahoo-Bonus", where we have discussed the possibility to assign a particular imporance to certain web pages. In the words of the Random Surfer Model, this is realized by increasing the probability for the Random Surfer jumping to a page after "getting bored". Via links, this manual intervention in the PageRank technique has an influence on the PageRank of each page on the web. More precisely, we have reached taking influence on PageRank by implementing another value E in the PageRank algorithm:

PR(A) = E(A) (1-d) + d (PR(T1)/C(T1) + ... + PR(Tn)/C(Tn))

Haveliwala's Topic-Sensitive-PageRank goes one step further. Instead of assigning a universally higher value to a website or a web page, Haveliwala differentiates on the basis of different topics. For each of these topics, he identifies other authority pages. On the basis of this evaluation, different PageRanks are calculated - each separately, but for the entire web.

For his experiments on Topic-Sensitive PageRank, Haveliwala has chosen the 16 top-level categories of the Open Directory Project both for the identification of topics and for the intervention in PageRank. More precisely, Haveliwala assigns a higher value E to the pages of those ODP categories for which he calculates PageRank. If, for example, he calculates the PageRank for the topic health, all the ODP pages in the health category receive a relatively higher value E and they pass this value in the form of PageRank on to the pages which are linked from there. Of course, this PageRank is passed on to other pages and, if we assume that health-related websites tend to link more often to other websites within that topic, pages on the topic health generally receive a higher PageRank.

Haveliwala confirms the incompleteness of choosing the Open Directory Project in order to identify topics, which for example results in a high degree of dependence on ODP editors and in a rather rough subdivision into topics. But, as Haveliwala states, his method shows good results and it can surely be improved without big effort.

However, one crucial point in Haveliwala's work on Topic-Sensitive-PageRank is the identification of the user's preferences. Having a Topic-Specific PageRank is useless as long as we do not know in which topics an actual user is interested. In the end, search results must be based on the PageRank that matches the user's preferences best. The Topic-Sensitive PageRank can only be used if these are known.

Indeed, Haveliwala does supply some practicable approaches for the identification of user preferences. He describes, for example, the search in context by highlighting terms on a web page. In this way, the content of that web page could be an indicator for waht the user is looking for. At this point, we want to note the potential of the Google Toolbar. The Toolbar submits data regarding search terms and pages that a user has visited to Google. This data can be used to create user profiles which can then be a basis for the identification of the user's preferences. However, even without using such techniques, it is imaginable that a user simply chooses the topic he is interested in before he does a query.

The Weighting of Links Based on Content Analyses:

That it is possible to weight single links within the PageRank technique has been shown on the previous page. The thought behind weighting links based on content analyses is to avoid the corrumption of PageRank. By weighting links this way, it is theoretically possible to diminish the influence of links between thematically unrelated page, which have been set for the sole purpose of boosting PageRank of one page. Indeed, it is questionable if it is possible to realize such weighting based on content analyses.

The fundamentals of content analyses are based on Gerard Salton's work in the 1960s and 1970s. In his vector space model of information retrieval, documents are modeled as vectors which are built upon terms and their weighting within the document. These term vectors allow comparisons between the content of documents by, for instance, calculating the cosine measure (the inner product) of the vectors. In its basic form, the vector space model has some weaknesses. For instance, often the assumption that if and in how far the same words appear in two documents is an indicator for their similarity is criticized. However, numerous enhancements have been developed that solve most of the problems of the vector space model.

One person who excelled at publications which are based on Salton's vector space model is Krishna Bharat. This is interesting because Bharat meanwhile is a member of Google's staff and, particularly, because he is deemded to be the developer of "Google News" ( Google News is a service that crawls news websites, evaluates articles and then provides them categorized and grouped in different subjects on the Google New website. According to Google, all these procedures are completely automated. Therefore, other criteria like, for example, the time when an article is published, are taken into account, but if there is no manual intervention, the clustering of articles is most certainly only possible, if the contents of the articles are actually compared to each other. The questions is: How can this be realized?

In their publication on a term vector database, Raymie Stata, Krishna Bharat and Farzin Maghoul describe how the contents of web pages can be compared based on term vectors and, particularly, they describe how some of the problems with the vector space model can be solved. Firstly, not all terms in documents are suitable for content analsysis. Very frequent terms provide only little discrimination across vectors and, so, the most frequent third of all terms is eliminated from the database. Infrequent terms, on the other hand, do not provide a good basis for measuring similarity. Such terms are, for example, misspellings. They appear only on few pages which are likely unrelated in terms of their theme, but because they are so infrequent, the term vectors of the pages appear to be closely related. Hence, also the least frequent third of all terms is eliminated from the database.

Even if only one third of all terms is included in the term vectors, this selection is still not very efficient. Stata, Bharat and Maghoul perform another filtering, so that each term vector is based on a maximum of 50 terms. But these are not the 50 most frequent terms on a page. They weight a term by deviding the number of times it appears on a page by the number of times it appears on all pages, and those 50 terms with the highest weight are included in the term vector of a page. This selection actually allows a real differentiation between the content of pages.

The methods described above are standards for the vector space model. If, for example, the inner product of two term vectors is rather high, the contents of the according pages tend to be similar. This may allow content comparisons in many areas, but it is doubtful if it is a good basis for weighting links within the PageRank technique. Most of all, synonyms and terms that describe similar things can not be identified. Indeed, there are algorithms for word stemming which work good for the english language, but in other languages word stemming is much more complicated. Different languages are a general problem. Unless, for instance, brand names or loan words are used, texts in different languages normally do not contain the same terms. And if they do, these terms normally have a completely different meaning, so that comparing content in different languages is not possible. However, Stata, Bharat and Maghoul provide a method of resolution for these problems.

Stata, Bharat und Maghoul present a concrete application for their Term Vector Database by classifying pages thematically. Bharat has also published on this issue together with Monika Henzinger, presently Google's Research Director, and they called it "topic distillation". Topic distillation is based on calculating so-called topic vectors. Topic vectors are term vectors, but they do not only include terms of one page but rather the terms of many pages which are on the same topic. So, in order to create topic vectors, they have to know a certain amount of web pages which are on several pre-defined topics. To achieve this, they resort to web directories.

For their application, Stata, Bharat und Maghoul have crawled about 30,000 links within each of the then 12 main categories of Yahoo to create topic vectors which include about 10,000 terms each. Then, in order to identify the topic of any other web page, they matched the according term vector with all the topic vectors which were created from the Yahoo crawl. The topic of a web page derived from the topic vector which matched the term vector of the web page best. That such a classification of web pages works can again be observed by the means of Google News. Google News does not only merge articles to one news topic, but also arranges them to the categories World, U.S., Business, Sci/Tech, Sports, Entertainment and Health. As long as this categorization is not based on the structure of the website where the articles come from (which is unlikely), the actual topic of an article has in fact to be computed.

At the time he published on term vectors, Krishna Bharat did not work on PageRank but rather on Kleinberg's algorithm, so that he was more interested in filtering off-topic links than in weighting links. But from classifying pages to weighting links based on content comparisons, there is only a small step. Instead of matching the term vectors of two pages, it is much more efficient to match the topics of two pages. We can, for instance, create a "topic affinity vector" for each page based on the degree of affinity of the page's term vector and all the topic vectors. The better the topic affinity vectors of two pages match, the more likely are they on the same topic and the higher should a link between them be weighted.

Using topic vectors has one big advantage over comparing term vectors directly: A topic vector can include terms in different languages by being based on, for instance, the links on different national Yahoo versions. Deviant site structures of the national versions can most certainly be adapted manually. Even better may be using the ODP because the structure of the sub-categories of the "World" category is based on the main OPD structure. In this way, measuring topic similarities between pages in different languages can be realized, so that a really useful weighting of links based on text analyses appears to be possible.

Is there an Actual Implementation of Themes in PageRank?

That both the approach of Havelivala and the approach of Richardson and Domingos are not utilized by Google is obvious. One would notice it using Google. However, a weighting of links based on text analyses would not be apparent immediately. It has been shown that it is theoretically possible. But it is doubtful that it is actually implemented.

We do not want to claim that we have shown the only way of weighting links on the basis of text analyses. Indeed, there are certainly dozens of others. However, the approach that we provided here is based on publications of important members of Google's staff and, thus, we want to rest a critical evaluation on it. Like always, when talking about PageRank, there is the question if our approach is sufficienly scalable. On the one hand, it causes additional memory requirements. After all, Stata, Bharat and Maghoul describe the system architecture of a term vector database which is different from Google's inverse index, since it maps from page ids to terms and, so, can hardly be integrated in the existing architecture. At the actual size of Google's index, the additional memory requirements should be several hundred GB to a few TB. However, this should not be so much of a problem since Google's index is most certainly several times bigger. In fact, the time requirements for building the database and for computing the weigtings appear to be the critical part.

Building a term verctor database should be approximately as time-consuming as building an inverse index. Of course, many procecces can probably be used for building both but if, for instance, the weighting of terms in the term vectors has to differ from the weighting of terms in the inverse index, the time requirements remain substantial. If we assume that, like in our approach, content analyses are based on computing the inner products of topic affinity vectors which have to be calculated by matching term vectors and topic vectors, this process should be approximately as time-consuming as computing PageRank. Moreover, we have to consider that the PageRank calculations themselves beome more complicated by weighting links.

So, the additional time requirements are definitely not negligible. This is why we have to ask ourselves if weighting links based on text analyses is useful at all. Links between thematically unrelated page, which have been set for the sole purpose of boosting PageRank of one page, may be annoying, but most certainly they are only a small fraction of all links. Additionally, the web itself is completely inhomogeneous. Google, Yahoo or the ODP do not owe their high PageRank solely to links from other search engines or directories. A huge part of the links on the web are simply not set for the purpose of showing visitors ways to more thematically related information. Indeed, the motivation for placing links is manifold. Moreover, the problably most popular websites are completely inhomogeneous in terms of theme. Think about portals like Yahoo or news websites which contain articles that cover almost any subject of life. A strong weighting of links as it has been described here could influence those website's PageRanks significantly. If the PageRank technique shall not become totally futile, a weighting of links can only take place to a small extent. This, of course, raises the question if the efforts it requires are justifiable. After all, there are certainly other ways to eliminate spam which often comes to the top of search results through thematically unrelated and probably bought links.

PR0 - Google's PageRank 0 Penalty:

By the end of 2001, the Google search engine introduced a new kind of penalty for websites that use questionable search engine optimization tactics: A PageRank of 0. In search engine optimization forums it is called PR0 and this term shall also be used here. Characteristically for PR0 is that all or at least a lot of pages of a website show a PageRank of 0 in the Google Toolbar, even if they do have high quality inbound links. Those pages are not completely removed from the index but they are always at the end of search results and, thus, they are hardly to be found.

A PageRank of 0 does not always mean a penalty. Sometimes, websites which seam to be penalized simply lack inbound links with an sufficiently high PageRank. But if pages of a website which have formerly been placed well in search results, suddenly show the dreaded white PageRank bar, and if there have not been any substantial changes regarding the inbound links of that website, this means - according to the prevailing opinion - certainly a penalty by Google.

We can do nothing but speculate about the causes for PR0 because Google representatives rarely publish new information on Google's algorithms. But, non the less, we want to give a theoretical approach for the way PR0 may work because of its serious effects on search engine optimization.

The Background of PR0

Spam has always been one of the biggest problems that search engines had to deal with. When spam is detected by search engines, the usual proceeding is the banishment of those pages, websites, domains or even IP addresses from the index. But, removing websites manually from the index always means a large assignment of personnel. This causes costs and definitely runs contrary to Google's scalability goals. So, it appears to be necessary to filter spam automatically.

Filtering spam automatically carries the risk of penalizing innocent webmasters and, hence, the filters have to react rather sensibly on potential spam. But then, a lot of spam can pass the filters and some additional measures may be necessary. In order to filter spam effectively, it might be useful to take a look at links.

That Google uses link analysis in order to detect spam has been confirmed more or less clearly in WebmasterWorld's Google News Forum by a Google employee who posts as "GoogleGuy". Over and over again, he advises webmasters to avoid "linking to bad neighbourhoods". In the following, we want to specify the "linking to bad neighbourhoods" and, to become more precisely, we want to discuss how an identification of spam can be realized by the analysis of link structures. In particular, it shall be shown how entire networks of spam pages, which may even be located on a lot of different domains, can be detected.

BadRank as the Opposite of PageRank

The theoretical approach for PR0 as it is presented here was initially brought up by Raph Levien ( We want to introduce a technique that - just like PageRank - analyzes link structures, but, that unlike PageRank does not determine the general importance of a web page but rather measures its negative characteristics. For the sake of simplicity this technique shall be called "BadRank".

BadRank is in priciple based on "linking to bad neighbourhoods". If one page links to another page with a high BadRank, the first page gets a high BadRank itself through this link. The similarities to PageRank are obvious. The difference is that BadRank is not based on the evaluation of inbound links of a web page but on its outbound links. In this sense, BadRank represents a reversion of PageRank. In a direct adaptation of the PageRank algorithm, BadRank would be given by the following formula:

BR(A) = E(A) (1-d) + d (BR(T1)/C(T1) + ... + BR(Tn)/C(Tn))


BR(A) is the BadRank of page A,
BR(Ti) is the BadRank of pages Ti which are outbound links of page A,
C(Ti) is here the number of inbound links of page Ti and
d is the again necessary damping factor.
In the previously discussed modifications of the PageRank algorithm, E(A) represented the special evaluation of certain web pages. Regarding the BadRank algorithm, this value reflects if a page was detected by a spam filter or not. Without the value E(A), the BadRank algorithm would be useless because it was nothing but another analysis of link structures which would not take any further criteria into account.

By means of the BadRank algorithm, first of all, spam pages can be evaluated. A filter assigns a numeric value E(A) to them, which can, for example, be based on the degree of spamming or maybe even better on their PageRank. Thereby, again, the sum of all E(A) has to equal the total number of web pages. In the course of an iterative computation, BadRank is not only transfered to pages which link to spam pages. In fact, BadRank is able to identify regions of the web where spam tends to occur relatively often, just as PageRank identifies regions of the web which are of general importance.

Of course, BadRank and PageRank have significant differences, especially, because of using outbound and inbound links, respectively. Our example shows a simple, hierarchically structured website that reflects common link structures pretty well. Each page links to every page which is on a higher hierachical level and on its branch of the website's tree structure. Each page links to pages which are arranged hierarchically directly below them and, additionally, pages on the same branch and the same hierarchical level link to each other.

The following table shows the distribution of inbound and outbound links for the hierarchical levels of such a site.
Level inbound Links outbound Links
0 6 2
1 4 4
2 2 3

As to be expected, regarding inbound links, a hierarchical gradation from the index page downwards takes place. In contrast, we find the highest number of outbound links on the website's mid-level. We can see similar results, when we add another level of pages to our website while the above described linking rules stay the same.

Level inbound Links outbound Links
0 14 2
1 8 4
2 4 5
3 2 4

Again, there is a concentration of outbound links on the website's mid-level. But most of all, the outbound links are much more evenly distributed than the inbound links. If we assign a value of 100 to the index page's E(A) in our original example, while all other values E equal 1 and if the damping factor d is 0.85, we get the following BadRank values:
Page BadRank
A 22.39
B/C 17.39
D/E/F/G 12.21

First of all, we see that the BadRank distributes from the index page among all other pages of the website. The combination of PageRank and BadRank will be discussed in detail below, but, no matter how the combination will be realized, it is obvious that both can neutralize each other very well. After all, we can assume that also the page's PageRank decreases, the lower the hierarchy level is, so that a PR0 can easily be achieved for all pages.

If we now assume that the hierarchically inferior page G links to a page X with a constant BadRank BR(X)=10, whereby the link from page G is the only inbound link for page X, and if all values E for our example website equal 1, we get, at a damping factor d of 0.85, the following values:
Page BadRank
A 4.82
B 7.50
C 14.50
D 4.22
E 4.22
F 11.22
G 17.18

In this case, we see that the distribution of the BadRank is less homogeneous than in the first scenario. Non the less, a distribution of BadRank among all pages of the website takes place. Indeed, the relatively low BadRank of the index page A is remarkable. It could be a problem to neutralize its PageRank which should be higher compared to the rest of the pages. This effect is not really desirable but it reflects the experiences of numerous webmasters. Quite often, we can see the phenomenom that all pages except for the index page of a website show a PR0 in the Google Toolbar, whereby the index page often has a Toolbar PageRank between 2 and 4. Therefore, we can probably assume that this special variant of PR0 is not caused by the detection of the according website by a spam filter, but the site rather received a penalty for "linking to bad neighbourhoods". Indeed, it is also possible that this variant of PR0 occurs when only hierarchical inferior pages of a website get trapped in a spam filter.

The Combination of PageRank and BadRank to PR0

If we assume that BadRank exists in the form presented here, there is now the question in which way BadRank and PageRank can be combined, in order to penalize as much spammers as possible while at the same time penalizing as few innocent webmasters as possible.

Intuitively, implementing BadRank directly in the actual PageRank computations seems to make sense. For instance, it is possible to calculate BadRank first and, then, divide a page's PageRank through its BadRank each time in the course of the iterative calculation of PageRank. This would have the advantage, that a page with a high BadRank could pass on just a little PageRank or none at all to the pages it links to. After all, one can argue that if one page links to a suspect page, all the other links on that page may also be suspect.

Indeed, such a direct connection between PageRank and BadRank is very risky. Most of all, the actual influence of BadRank on PageRank cannot be estimated in advance. It is to be considered that we would create a lot of pages which cannot pass on PageRank to the pages they link to. In fact, these pages are dangling links, and as it has been discussed in the section on outbound links, it is absolutely necessary to avoid dangling links while computing PageRank.

So, it would be advisable to have separate iterative calculations for PageRank and BadRank. Combining them afterwards can, for instance, be based on simple arithmetical operations. In principle, a subtraction would have the desirable consequence that relatively small BadRank values can hardly have a large influence on relatively high PageRank values. But, there would certainly be a problem to achieve PR0 for a large number of pages by using the subtraction. We would rather see a PageRank devaluation for many pages.

Achieving the effects that we know as PR0 seems easier to be realized by dividing PageRank through BadRank. But this would imply that BadRank receives an extremely high importance. However, since the average BadRank equals 1, a big part of BadRank values is smaller than 1 and, so, a normalization is necessary. Probably, normalizing and scaling BadRank to values between 0 and 1 so that "good" pages have values close to 1, and "bad" pages have values close to 0 and, subsequently, multiplying these values with PageRank would supply the best results.

A very effective and easy to realize alternative would probably be a simple stepped evaluation of PageRank and BadRank. It would be reasonable that if BadRank exceeds a certain value it will always lead to a PR0. The same could happen when the relation of PageRank to BadRank is below a certain value. Additionally, it would make sense that if BadRank and/or the relation of BadRank to PageRank is below a certain value, BadRank takes no influence at all.

Only if none of these cases occurs, an actual combination of PageRank and BadRank - for instance by dividing PageRank through BadRank - would be necessary. In this way, all unwanted effects could be avoided.

A Critical View on BadRank and PR0

How Google would realize the combination of PageRank and BadRank is of rather minor importance. Indeed, a separate computation and a subsequent combination of both has the consequence that it may not be possible to see the actual effect of a high BadRank by looking at the Toolbar. If a page has a high PageRank in the original sense, the influence of its BadRank can be negligible. But if another page links to it, this could have quite serious consequences.

An even bigger problem is the direct reversion of the PageRank algorithm as we have presented it here: Just as an additional inbound for one page can do nothing but increasing this page's PageRank, an additional outbound link can only increase its BadRank. This is because of the addition of BadRank values in the BadRank formula. So, it does not matter how many "good" outbound links a page has - one link to a spam page can be enough to lead to a PR0.

Indeed, this problem may appear in exceptional cases only. By our direct reversion of the PageRank algorithm, the BadRank of a page is divided by its inbound links and single links to pages with high BadRank transfer only a part of that BadRank in each case. Google's Matt Cutts' remark on this issue is: "If someone accidentally does a link to a bad site, that may not hurt them, but if they do twenty, that's a problem." (

However, as long as all links are weighted uniformly within the BadRank computation, there is another problem. If two pages differ widely in PageRank and both have a link to the same page with a high BadRank, this may lead to the page with the higher PageRank suffering far less from the transferred BadRank than the page with the low PageRank. We have to hope that Google knows how to deal with such problems. Nevertheless it shall be noted that, regarding the procedure presented here, outbound links can do nothing but harm.

Of course, all statements regarding how PR0 works are pure speculation. But in principle, the analysis of link structures similarly to the PageRank technique should be the way how only Google understands to deal with spam.


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