Results 1  10
of
31
Identifying Opinion Leaders in the Blogosphere
"... Opinion leaders are those who bring in new information, ideas, and opinions, then disseminate them down to the masses, and thus influence the opinions and decisions of others by a fashion of word of mouth. Opinion leaders capture the most representative opinions in the social network, and consequent ..."
Abstract

Cited by 28 (0 self)
 Add to MetaCart
(Show Context)
Opinion leaders are those who bring in new information, ideas, and opinions, then disseminate them down to the masses, and thus influence the opinions and decisions of others by a fashion of word of mouth. Opinion leaders capture the most representative opinions in the social network, and consequently are important for understanding the massive and complex blogosphere. In this paper, we propose a novel algorithm called InfluenceRank to identify opinion leaders in the blogosphere. The InfluenceRank algorithm ranks blogs according to not only how important they are as compared to other blogs, but also how novel the information they can contribute to the network. Experimental results indicate that our proposed algorithm is effective in identifying influential opinion leaders.
PageRank: Functional Dependencies
"... 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 α that spreads uniformly part of the rank. The choice of α is eminently empirical, and in most cases the original suggestion α = 0.85 ..."
Abstract

Cited by 15 (5 self)
 Add to MetaCart
(Show Context)
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 α that spreads uniformly part of the rank. The choice of α is eminently empirical, and in most cases the original suggestion α = 0.85 by Brin and Page is still used. In this paper, we give a mathematical analysis of PageRank when α changes. In particular, we show that, contrarily to popular belief, for realworld graphs values of α close to 1 do not give a more meaningful ranking. Then, we give closedform formulae for PageRank derivatives of any order, and by proving that the kth iteration of the Power Method gives exactly the value obtained by truncating the PageRank power series at degree k, we show how to obtain an approximation of the derivatives. Finally, we view PageRank as a linear operator acting on the preference vector and show a tight connection between iterated computation and derivation.
Traps and pitfalls of topicbiased PageRank
 In WAW 2006. Fourth Workshop on Algorithms and Models for the WebGraph, Lecture Notes in Computer Science
, 2007
"... We discuss a number of issues in the definition, computation and comparison of PageRank values that have been addressed sparsely in the literature, often with contradictory approaches. We study the difference between weakly and strongly preferential PageRank, which patch the dangling nodes with diff ..."
Abstract

Cited by 12 (3 self)
 Add to MetaCart
(Show Context)
We discuss a number of issues in the definition, computation and comparison of PageRank values that have been addressed sparsely in the literature, often with contradictory approaches. We study the difference between weakly and strongly preferential PageRank, which patch the dangling nodes with different distributions, extending analytical formulae known for the strongly preferential case, and corroborating our results with experiments on a snapshot of 100 millions of pages of the.uk domain. The experiments show that the two PageRank versions are poorly correlated, and results about each one cannot be blindly applied to the other; moreover, our computations highlight some new concerns about the usage of exchangebased correlation indices (such as Kendall’s τ) on approximated rankings. 1
BioMed Central
, 2009
"... Subjective versus objective risk in genetic counseling for hereditary breast and/or ovarian cancers ..."
Abstract

Cited by 11 (3 self)
 Add to MetaCart
(Show Context)
Subjective versus objective risk in genetic counseling for hereditary breast and/or ovarian cancers
PAGERANK COMPUTATION, WITH SPECIAL ATTENTION TO DANGLING NODES
"... Abstract. We present a simple algorithm for computing the PageRank (stationary distribution) of the stochastic Google matrix G. The algorithm lumps all dangling nodes into a single node. We express lumping as a similarity transformation of G, and show that the PageRank of the nondangling nodes can b ..."
Abstract

Cited by 11 (0 self)
 Add to MetaCart
(Show Context)
Abstract. We present a simple algorithm for computing the PageRank (stationary distribution) of the stochastic Google matrix G. The algorithm lumps all dangling nodes into a single node. We express lumping as a similarity transformation of G, and show that the PageRank of the nondangling nodes can be computed separately from that of the dangling nodes. The algorithm applies the power method only to the smaller lumped matrix, but the convergence rate is the same as that of the power method applied to the full matrix G. The efficiency of the algorithm increases as the number of dangling nodes increases. We also extend the expression for PageRank and the algorithm to more general Google matrices that have several different dangling node vectors, when it is required to distinguish among different classes of dangling nodes. We also analyze the effect of the dangling node vector on the PageRank, and show that the PageRank of the dangling nodes depends strongly on that of the nondangling nodes but not vice versa. At last we present a Jordan decomposition of the Google matrix for the (theoretical) extreme case when all web pages are dangling nodes.
Asynchronous iterative computations with Web information retrieval structures: The PageRank case
, 2005
"... ..."
(Show Context)
Using polynomial chaos to compute the influence of multiple random surfers in the PageRank model
 Proceedings of the 5th Workshop on Algorithms and Models for the Web Graph (WAW2007), volume 4863 of Lecture Notes in Computer Science
, 2007
"... Abstract. The PageRank equation computes the importance of pages in a web graph relative to a single random surfer with a constant teleportation coefficient. To be globally relevant, the teleportation coefficient should account for the influence of all users. Therefore, we correct the PageRank form ..."
Abstract

Cited by 8 (5 self)
 Add to MetaCart
(Show Context)
Abstract. The PageRank equation computes the importance of pages in a web graph relative to a single random surfer with a constant teleportation coefficient. To be globally relevant, the teleportation coefficient should account for the influence of all users. Therefore, we correct the PageRank formulation by modeling the teleportation coefficient as a random variable distributed according to user behavior. With this correction, the PageRank values themselves become random. We present two methods to quantify the uncertainty in the random PageRank: a Monte Carlo sampling algorithm and an algorithm based the truncated polynomial chaos expansion of the random quantities. With each of these methods, we compute the expectation and standard deviation of the PageRanks. Our statistical analysis shows that the standard deviation of the PageRanks are uncorrelated with the PageRank vector. 1
A deeper investigation of PageRank as a function of the damping factor
 In Web Information Retrieval and Linear Algebra Algorithms, number 07071 in Dagstuhl Seminar Proceedings
, 2007
"... 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 α that spreads uniformly part of the rank. The choice of α is eminently empirical, and in most cases the original suggestion α = 0.85 ..."
Abstract

Cited by 7 (1 self)
 Add to MetaCart
(Show Context)
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 α that spreads uniformly part of the rank. The choice of α is eminently empirical, and in most cases the original suggestion α = 0.85 by Brin and Page is still used. In this paper, we give a mathematical analysis of PageRank when α changes. In particular, we show that, contrarily to popular belief, for realworld graphs values of α close to 1 do not give a more meaningful ranking. Then, we give closedform formulae for PageRank derivatives of any order, and by proving that the kth iteration of the Power Method gives exactly the PageRank value obtained using a Maclaurin polynomial of degree k, we show how to obtain an approximation of the derivatives. Finally, we view PageRank as a linear operator acting on the preference vector and show a tight connection between iterated computation and derivation. 1
Casati F. Exploring and understanding citationbased scientific metrics
 First International Conference Complex 2009, Revised Papers, Part 2
"... This paper explores citationbased metrics, how they differ in ranking papers and authors, and why. We initially take as example three main metrics that we believe significant; the standard citation count, the more and more popular hindex, and a variation we propose of PageRank applied to papers (c ..."
Abstract

Cited by 6 (4 self)
 Add to MetaCart
(Show Context)
This paper explores citationbased metrics, how they differ in ranking papers and authors, and why. We initially take as example three main metrics that we believe significant; the standard citation count, the more and more popular hindex, and a variation we propose of PageRank applied to papers (called PaperRank), that is appealing as it mirrors proven and successful algorithms for ranking web pages. As part of analyzing them, we develop generally applicable techniques and metrics for qualitatively and quantitatively analyzing indexes that evaluate content and people, as well as for understanding the causes of their different behaviors. Finally, we extend the analysis to other popular indexes, to show whether the choice of the index has a significant effect in how papers and authors are ranked. We put the techniques at work on a dataset of over 260K ACM papers, and discovered that the difference in ranking results is indeed very significant (even when restricting to citationbased indexes), with half of the topranked papers differing in a typical 20element long search result page for papers on a given topic, and with the top researcher being ranked differently over half of the times in an average job posting with 100 applicants.
PageRank Problem, Survey And Future Research Directions
"... In this survey, we provide the most important computational methods to find the PageRank. This is a new comprehensive review of all major issues which are associated with PageRank problem, covering the basic topics, the iterative methods, lumping of nodes, the modification of lumping the nodes, ran ..."
Abstract

Cited by 5 (0 self)
 Add to MetaCart
(Show Context)
In this survey, we provide the most important computational methods to find the PageRank. This is a new comprehensive review of all major issues which are associated with PageRank problem, covering the basic topics, the iterative methods, lumping of nodes, the modification of lumping the nodes, rankone perturbation, rankr perturbation, advanced numerical linear algebra methods, conditioning, a new method by power series, and outlines for future studies.