# page rank Published articles for page rank. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How Google PageRank Can Be Manipulated Through Manufactured Backlinks DevFeed: [How Google PageRank Can Be Manipulated Through Manufactured Backlinks]() Original publisher: [Read original article]() Published: 2011-06-21T17:25:08Z Content type: opinion Language: en Sources: [Jeremy Kun]() Topics: [Google Search](), [Algorithm](), [clients]() Tags: [algorithm](), [fraud](), [google](), [google-search](), [history](), [page-rank](), [ranking]() ### AI overview The article examines how Google PageRank can be manipulated when businesses manufacture backlinks through controversy and negative publicity. Using the DecorMyEyes case, it argues that PageRank can rank pages inappropriately when sites compete to influence the system. ### Source excerpt A Bully By Any Other Name From the New York Times: "Shopping online in late July, Clarabelle Rodriguez typed the name of her favorite eyeglass brand into Google's search bar. In moments, she found the perfect frames -- made by a French company called Lafont -- on a Web site that looked snazzy and stood at the top of the search results. Not the tippy-top, where the paid ads are found, but under those, on Google's version of the gold-medal podium, where the most relevant and popular site is displayed. ## Google's Page Rank--The Final Product DevFeed: [Google's Page Rank--The Final Product]() Original publisher: [Read original article]() Published: 2011-06-20T22:07:41Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Google](), [Matrix](), [math](), [Web]() Tags: [computational-complexity](), [eigenvalues](), [eigenvectors](), [google](), [mathematics](), [matrix](), [page-rank](), [probability-theory](), [programming](), [pseudocode](), [ranking](), [search-engine](), [web]() ### AI overview This article explains how PageRank addresses dangling nodes and non-unique rankings. It introduces positive column-stochastic matrices and the Perron-Frobenius Theorem, then uses random web surfing as a probability-based intuition for webpage importance. ### Source excerpt Dangling Nodes and Non-Uniqueness Recall where we left off last time. Given a web $ W$ with no dangling nodes, the link matrix for $ W$ has 1 as an eigenvalue, and if the corresponding eigenspace has dimension 1, then any associated eigenvector gives a ranking of the pages in $ W$ which is consistent with our goals. The first problem is that if there is a dangling node, our link matrix has a column of all zeros, and is no longer column-stochastic. ## Google's PageRank--A First Attempt DevFeed: [Google's PageRank--A First Attempt]() Original publisher: [Read original article]() Published: 2011-06-18T18:05:20Z Content type: article Language: en Sources: [Jeremy Kun]() Topics: [Graphs](), [graph theory](), [Web](), [Algorithm](), [Internet](), [structure]() Tags: [algorithm](), [analysis](), [eigenvalues](), [eigenvectors](), [google](), [graph](), [graph-theory](), [internet](), [mathematics](), [page-rank](), [ranking](), [search-engine](), [structure](), [vertex](), [web]() ### AI overview This post models the Web as a directed graph and introduces PageRank-style importance scoring for web pages. It first considers ranking pages by incoming-link counts, then explains why treating every link as equally valuable is inadequate and motivates weighting links by the importance of the linking page. ### Source excerpt The Web as a Graph The goal of this post is to assign an "importance score" $ x_i \in [0,1]$ to each of a set of web pages indexed $ v_i$ in a way that consistently captures our idea of which websites are likely to be important. But before we can extract information from the structure of the internet, we need to have a mathematical description of that structure. Enter graph theory. ## Google's PageRank--Introduction DevFeed: [Google's PageRank--Introduction]() Original publisher: [Read original article]() Published: 2011-06-12T21:09:29Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Google](), [Internet](), [Algorithm](), [Web]() Tags: [algorithm](), [crawler](), [google](), [history](), [indexer](), [page-rank](), [query-engine](), [ranking](), [retrieval](), [search-engine](), [sorting]() ### AI overview An introduction to Google's PageRank and the broader process search engines use to select and order information. The article describes crawlers, indexers, ranking algorithms, and query engines, and frames ranking in terms of popularity and authority rather than relevance alone. ### Source excerpt Importance on the Web As a society living in the "Information Age," it comes as no surprise that we are faced with the task of sorting through vast oceans of content. With the admission that most content is actually junk, we must wisely choose the objects of our analysis. The appropriately named site UselessJunk.com certainly doesn't deserve the same attention as the BBC World News page, and yet within the monstrous heart of the internet, it requires the maturity of the human psyche to discriminate their relative worth.