A Singular Perturbation Approach for Choosing the PageRank Damping Factor

Avrachenkov, Konstantin; Litvak, Nelly; Pham, Kim

doi:10.1080/15427951.2008.10129300

Cited by 33 publications

(25 citation statements)

References 17 publications

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“…The reason for this is that at higher values for α the peripheral parts of the citation network receive too much weight relative the more important central ones, which as a consequence become underrated (cf. Boldi et al, 2005;Avrachenko et al, 2008). of α close to 0.5.…”

Section: Indicatorsmentioning

confidence: 99%

Using PageRank in the analysis of technological progress through patents: an illustration for biotechnological inventions

Reinstaller

Reschenhofer

2017

Scientometrics

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This paper examines whether PageRank algorithms are a valid instrument for the analysis of technical progress in specific technological fields by means of patent citation data. It provides evidence for patent data in biotechnology. Recent literature has been critical with regard to the use of PageRank for the analysis of scientific citation networks. The results reported in this paper indicate, however, that with some minor adaptations and careful interpretation of the results the algorithm can be used to capture some important stylised facts of technical progress and the importance of single patents relatively well especially if compared to indicators based on direct inward citations only. The results reported in this paper indicate, however, that with some minor adaptations and careful interpretation of the results the algorithm can be used to capture some important stylised facts of technical progress and the importance of single patents relatively well especially if compared to indicators based on direct inward citations only.

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Section: Indicatorsmentioning

confidence: 99%

Using PageRank in the analysis of technological progress through patents: an illustration for biotechnological inventions

Reinstaller

Reschenhofer

2017

Scientometrics

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“…(7) [8,25]: (7) Where is the teleportation matrix of , controls priority given to the hyperlink structure over artificial teleportation matrix . Many researchers state that the value of damping factor affect the convergence rate of PageRank Power method [11,12,14,18,19]. We have performed an experiment to observe the impact of damping factor on the convergence rate of PageRank algorithm, and ordering of web pages displayed on web search engine on various datasets by using following Eq.…”

Section: Effect Of Damping Factor On Pagerank Algorithmmentioning

confidence: 99%

“…In this experiment we have taken, only top 25 web pages return by PageRank method because SERP (Search engine result pages) contains almost 20 to 30 web pages in their first result page that are more relevant to the web user, and on average 90% users do not go beyond the first result page for any particular query [9,11]. If any web page indexed in the top 25 position for but cannot get position in top 25 for , then we have taken a parameter to show that whether page is indexed in top 25 or not, parameter is defined as follows:…”

Section: Damping Factor Value Versus Convergence Ratementioning

confidence: 99%

“…According to them PageRank can be stated as follows: Let a random web user starts surfing from an arbitrary web page, and at every time they navigate to next web page by selecting one of the hyperlinks from the current web page. In initial approximation, we could define PageRank as the ratio of [8,11,12,13]. To resolve rank sink issue, at every step the random web user has the choice to choose any out-link with probability , and will restart from another node of n web pages chosen at random with probability , where is a damping factor [7,14].…”

Section: Introductionmentioning

confidence: 99%

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Discussion on Damping Factor Value in PageRank Computation

Srivastava¹,

Garg²,

Mishra³

2017

IJISA

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Abstract-Web search engines use various ranking methods to determine the order of web pages displayed on the Search Engine Result Page (SERP). PageRank is one of the popular and widely used ranking method. PageRank of any web page can be defined as a fraction of time a random web surfer spends on that web page on average. The PageRank method is a stationary distribution of a stochastic method whose states are web pages of the Web graph. This stochastic method is acquired by combining the hyperlink matrix of the web graph and a trivial uniform process. This combination is needed to make primitive so that stationary distribution is well defined. The combination depends on the value of damping factor in the computation of PageRank. The damping factor parameter state that how much time random web surfer follow hyperlink structure than teleporting. The value of is exceptionally empirical and in current scenario = 0.85 is considered as suggested by Brin and Page. If we take α =0.8 then we can say that out of total time, 80% of time is taken by the random web surfer to follow the hyperlink structure and 20% time they teleport to new web pages randomly. Today web surfer gets worn out too early on the web because of non-availability of relevant information and they can easily teleport to new web pages rather than following hyperlink structure. So we have to choose some value of damping factor other than 0.85. In this paper, we have given an experimental analysis of PageRank computation for different value of the damping factor. We have observed that for value of =0.7, PageRank method takes fewer numbers of iterations to converge than =0.85, and for these values of the top 25 web pages returned by PageRank method in the SERP are almost same, only some of them exchange their positions. From the experimental results it is observed that value of damping factor =0.7 takes approximate 25-30% fewer numbers of iterations than =0.85 to get closely identical web pages in top 25 result pages for personalized web search, selective crawling, intra-web search engine.

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“…Most researchers take α = 0.85 [6]. Recently, Avrachenkov et al suggested choosing α = 1/2 [7]. Their suggestion follows from graph theoretic properties of the PageRank solution vector as a function of α.…”

Section: Introductionmentioning

confidence: 99%

Using Polynomial Chaos to Compute the Influence of Multiple Random Surfers in the PageRank Model

Constantine¹,

Gleich²

Algorithms and Models for the Web-Graph

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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.

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A Singular Perturbation Approach for Choosing the PageRank Damping Factor

Cited by 33 publications

References 17 publications

Using PageRank in the analysis of technological progress through patents: an illustration for biotechnological inventions

Using PageRank in the analysis of technological progress through patents: an illustration for biotechnological inventions

Discussion on Damping Factor Value in PageRank Computation

Using Polynomial Chaos to Compute the Influence of Multiple Random Surfers in the PageRank Model

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