blackhat blog

Yo, put on a big black hat, lol.

Change, impermanence, transition pervade us and everything we do. Thus, everything is gray. Yet, we demand that things be either black or white, unless it better suits you to delay decision… in which case you’ll see nothing but shades of hazy gray wherever you look — which works really well for some people, too… @

FROM: ecomatrix.eu

Mathematics
A simple variant of the TrustRank algorithm is given by

 TrustRank = M^(-1) *  X
where the matrix M (analogue to the PageRank algorithm) is given by
 M = 1 – d  T
with
 T_ij = 1 / C_j (if page j is linking to page i)
 T_ij = 0 (otherwise)
d is a damping factor and X the source vector of the trust.

The inverse PageRank is given by

 M_inv^(-1) * X_inv
with
 M_inv = 1 –  d_inv  T_inv
The inverse transition matrix T_inv is definied by
 T_ij = 1 /  n_j (if page i is linking to page  j)
 T_ij = 0 (otherwise)
d_inv is an additional damping factor, X_inv is the source vector of the spam (the bad pages) and n_j ist he number of incoming links on page j. M_inv is nether the transponent nor the inverse matrix of M.

FROM: http://www.google.com/technology/

PageRank relies on the uniquely democratic nature of the web by using its vast link structure as an indicator of an individual page’s value. In essence, Google interprets a link from page A to page B as a vote, by page A, for page B. But, Google looks at more than the sheer volume of votes, or links a page receives; it also analyzes the page that casts the vote. Votes cast by pages that are themselves “important” weigh more heavily and help to make other pages “important.”