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Can social bookmarking improve web search
 in Proceedings of the International Conference on Web Search and Web Data Mining (WSDM'08), ACM
"... Social bookmarking is a recent phenomenon which has the potential to give us a great deal of data about pages on the web. One major question is whether that data can be used to augment systems like web search. To answer this question, over the past year we have gathered what we believe to be the lar ..."
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Cited by 88 (5 self)
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Social bookmarking is a recent phenomenon which has the potential to give us a great deal of data about pages on the web. One major question is whether that data can be used to augment systems like web search. To answer this question, over the past year we have gathered what we believe to be the largest dataset from a social bookmarking site yet analyzed by academic researchers. Our dataset represents about forty million bookmarks from the social bookmarking site del.icio.us. We contribute a characterization of posts to del.icio.us: how many bookmarks exist (about 115 million), how fast is it growing, and how active are the URLs being posted about (quite active). We also contribute a characterization of tags used by bookmarkers. We found that certain tags tend to gravitate towards certain domains, and vice versa. We also found that tags occur in over 50 percent of the pages that they annotate, and in only 20 percent of cases do they not occur in the page text, backlink page text, or forward link page text of the pages they annotate. We conclude that social bookmarking can provide search data not currently provided by other sources, though it may currently lack the size and distribution of tags necessary to make a significant impact. 1.
Know your neighbors: Web spam detection using the web topology
 In Proceedings of SIGIR
, 2007
"... Web spam can significantly deteriorate the quality of search engine results. Thus there is a large incentive for commercial search engines to detect spam pages efficiently and accurately. In this paper we present a spam detection system that uses the topology of the Web graph by exploiting the link ..."
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Cited by 70 (9 self)
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Web spam can significantly deteriorate the quality of search engine results. Thus there is a large incentive for commercial search engines to detect spam pages efficiently and accurately. In this paper we present a spam detection system that uses the topology of the Web graph by exploiting the link dependencies among the Web pages, and the content of the pages themselves. We find that linked hosts tend to belong to the same class: either both are spam or both are nonspam. We demonstrate three methods of incorporating the Web graph topology into the predictions obtained by our base classifier: (i) clustering the host graph, and assigning the label of all hosts in the cluster by majority vote, (ii) propagating the predicted labels to neighboring hosts, and (iii) using the predicted labels of neighboring hosts as new features and retraining the classifier. The result is an accurate system for detecting Web spam that can be applied in practice to largescale Web data.
SpamRank  Fully Automatic Link Spam Detection
 IN PROCEEDINGS OF THE FIRST INTERNATIONAL WORKSHOP ON ADVERSARIAL INFORMATION RETRIEVAL ON THE WEB (AIRWEB
, 2005
"... Spammers intend to increase the PageRank of certain spam pages by creating a large number of links pointing to them. We propose a novel method based on the concept of personalized PageRank that detects pages with an undeserved high PageRank value without the need of any kind of white or blacklists ..."
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Cited by 67 (5 self)
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Spammers intend to increase the PageRank of certain spam pages by creating a large number of links pointing to them. We propose a novel method based on the concept of personalized PageRank that detects pages with an undeserved high PageRank value without the need of any kind of white or blacklists or other means of human intervention. We assume that spammed pages have a biased distribution of pages that contribute to the undeserved high PageRank value. We define SpamRank by penalizing pages that originate a suspicious PageRank share and personalizing PageRank on the penalties. Our method is tested on a 31 M page crawl of the .de domain with a manually classified 1000page stratified random sample with bias towards large PageRank values.
A survey on pagerank computing
 Internet Mathematics
, 2005
"... Abstract. This survey reviews the research related to PageRank computing. Components of a PageRank vector serve as authority weights for web pages independent of their textual content, solely based on the hyperlink structure of the web. PageRank is typically used as a web search ranking component. T ..."
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Cited by 64 (0 self)
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Abstract. This survey reviews the research related to PageRank computing. Components of a PageRank vector serve as authority weights for web pages independent of their textual content, solely based on the hyperlink structure of the web. PageRank is typically used as a web search ranking component. This defines the importance of the model and the data structures that underly PageRank processing. Computing even a single PageRank is a difficult computational task. Computing many PageRanks is a much more complex challenge. Recently, significant effort has been invested in building sets of personalized PageRank vectors. PageRank is also used in many diverse applications other than ranking. We are interested in the theoretical foundations of the PageRank formulation, in the acceleration of PageRank computing, in the effects of particular aspects of web graph structure on the optimal organization of computations, and in PageRank stability. We also review alternative models that lead to authority indices similar to PageRank and the role of such indices in applications other than web search. We also discuss linkbased search personalization and outline some aspects of PageRank infrastructure from associated measures of convergence to link preprocessing. 1.
A reference collection for Web spam
 SIGIR Forum
, 2006
"... We describe the WEBSPAMUK2006 collection, a large set of Web pages that have been manually annotated with labels indicating if the hosts are include Web spam aspects or not. This is the first publicly available Web spam collection that includes page contents and links, and that has been labelled by ..."
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Cited by 49 (13 self)
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We describe the WEBSPAMUK2006 collection, a large set of Web pages that have been manually annotated with labels indicating if the hosts are include Web spam aspects or not. This is the first publicly available Web spam collection that includes page contents and links, and that has been labelled by a large and diverse set of judges. 1
LinkBased Characterization and Detection of Web Spam
 In AIRWeb
, 2006
"... We perform a statistical analysis of a large collection of Web pages, focusing on spam detection. We study several metrics such as degree correlations, number of neighbors, rank propagation through links, TrustRank and others to build several automatic web spam classifiers. This paper presents a stu ..."
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Cited by 48 (8 self)
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We perform a statistical analysis of a large collection of Web pages, focusing on spam detection. We study several metrics such as degree correlations, number of neighbors, rank propagation through links, TrustRank and others to build several automatic web spam classifiers. This paper presents a study of the performance of each of these classifiers alone, as well as their combined performance. Using this approach we are able to detect 80.4% of the Web spam in our sample, with only 1.1% of false positives.
PageRank as a Function of the Damping Factor
, 2005
"... PageRank is defined as the stationary state of a Markov chain. The chain is obtained by perturbing the transition matrix induced by a web graph with a damping factor # that spreads uniformly part of the rank. The choice of # is eminently empirical, and in most cases the original suggestion # = 0.85 ..."
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Cited by 36 (9 self)
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PageRank is defined as the stationary state of a Markov chain. The chain is obtained by perturbing the transition matrix induced by a web graph with a damping factor # that spreads uniformly part of the rank. The choice of # is eminently empirical, and in most cases the original suggestion # = 0.85 by Brin and Page is still used. Recently, however, the behaviour of PageRank with respect to changes in # was discovered to be useful in linkspam detection [21]. Moreover, an analytical justification of the value chosen for # is still missing. In this paper, we give the first mathematical analysis of PageRank when # changes. In particular, we show that, contrarily to popular belief, for realworld graphs values of # close to 1 do not give a more meaningful ranking. Then, we give closedform formulae for PageRank derivatives of any order, and an extension of the Power Method that approximates them with convergence O for the kth derivative. Finally, we show a tight connection between iterated computation and analytical behaviour by proving that the kth iteration of the Power Method gives exactly the PageRank value obtained using a Maclaurin polynomial of degree k. The latter result paves the way towards the application of analytical methods to the study of PageRank.
A uniform approach to accelerated pagerank computation
 In WWW
, 2005
"... In this note we consider a simple reformulation of the traditional power iteration algorithm for computing the stationary distribution of a Markov chain. Rather than communicate their current probability values to their neighbors at each step, nodes instead communicate only changes in probability va ..."
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Cited by 31 (0 self)
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In this note we consider a simple reformulation of the traditional power iteration algorithm for computing the stationary distribution of a Markov chain. Rather than communicate their current probability values to their neighbors at each step, nodes instead communicate only changes in probability value. This reformulation enables a large degree of flexibility in the manner in which nodes update their values, leading to an array of optimizations and features, including faster convergence, efficient incremental updating, and a robust distributed implementation. While the spirit of many of these optimizations appear in previous literature, we observe several cases where this unification simplifies previous work, removing technical complications and extending their range of applicability. We implement and measure the performance of several optimizations on a sizable (34M node) web subgraph, seeing significant composite performance gains, especially for the case of incremental recomputation after changes to the web graph.
Generalizing pagerank: Damping functions for linkbased ranking algorithms
 In Proceedings of ACM SIGIR
"... This paper introduces a family of linkbased ranking algorithms that propagate page importance through links. In these algorithms there is a damping function that decreases with distance, so a direct link implies more endorsement than a link through a long path. PageRank is the most widely known ran ..."
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Cited by 29 (8 self)
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This paper introduces a family of linkbased ranking algorithms that propagate page importance through links. In these algorithms there is a damping function that decreases with distance, so a direct link implies more endorsement than a link through a long path. PageRank is the most widely known ranking function of this family. The main objective of this paper is to determine whether this family of ranking techniques has some interest per se, and how different choices for the damping function impact on rank quality and on convergence speed. Even though our results suggest that PageRank can be approximated with other simpler forms of rankings that may be computed more efficiently, our focus is of more speculative nature, in that it aims at separating the kernel of PageRank, that is, linkbased importance propagation, from the way propagation decays over paths. We focus on three damping functions, having linear, exponential, and hyperbolic decay on the lengths of the paths. The exponential decay corresponds to PageRank, and the other functions are new. Our presentation includes algorithms, analysis, comparisons and experiments that study their behavior under different parameters in real Web graph data. Among other results, we show how to calculate a linear approximation that induces a page ordering that is almost identical to PageRank’s using a fixed small number of iterations; comparisons were performed using Kendall’s τ on large domain datasets.
Characterization of national Web domains
 ACM Transactions on Internet Technology
, 2005
"... During the last few years, several studies on the characterization of the public Web space of various national domains have been published. The pages of a country are an interesting set for studying the characteristics of the Web, because at the same time these are diverse (as they are written by se ..."
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Cited by 28 (9 self)
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During the last few years, several studies on the characterization of the public Web space of various national domains have been published. The pages of a country are an interesting set for studying the characteristics of the Web, because at the same time these are diverse (as they are written by several authors) and yet rather similar (as they share a common geographical, historical and cultural context). This paper discusses the methodologies used for presenting the results of Web characterization studies, including the granularity at which different aspects are presented, and a separation of concerns between contents, links, and technologies. Based on this, we present a sidebyside comparison of the results of 12 Web characterization studies comprising over 120 million pages from 24 countries. The comparison unveils similarities and differences between the collections, and sheds light on how certain results of a single Web characterization study on a sample may be valid in the context of the full Web.