CiteULike: pdlug's ranking

Eigenfactor : Does the Principle of Repeated Improvement Result in Better Journal Impact Estimates than Raw Citation Counts?

Philip Davis — 2008-07-21T16:19:19-00:00

(17 Jul 2008)

Eigenfactor.org, a journal evaluation tool which uses an iterative algorithm to weight citations (similar to the PageRank algorithm used for Google) has been proposed as a more valid method for calculating the impact of journals. The purpose of this brief communication is to investigate whether the principle of repeated improvement provides different rankings of journals than does a simple unweighted citation count (the method used by ISI).

A Fast Algorithm for Learning a Ranking Function from Large-Scale Data Sets

Vikas Raykar — 2008-06-13T16:58:45-00:00

Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 30, No. 7. (2008), pp. 1158-1170.

We consider the problem of learning the ranking function that maximizes a generalization of the Wilcoxon-Mann-Whitney statistic on the training data. Relying on an $epsilon$-accurate approximation for the error-function, we reduce the computational complexity of each iteration of a conjugate gradient algorithm for learning ranking functions from $mathcalO(m^2)$, to $mathcalO(m)$, where $m$ is the number of training samples. Experiments on public benchmarks for ordinal regression and collaborative filtering indicate that the proposed algorithm is as accurate as the best available methods in terms of ranking accuracy, when the algorithms are trained on the same data. However, since it is several orders of magnitude faster than the current state-of-the-art approaches, it is able to leverage much larger training datasets.

Respect My Authority! HITS Without Hyperlinks, Utilizing Cluster-Based Language Models

Oren Kurland — 2008-05-02T06:59:24-00:00

(22 Apr 2008)

We present an approach to improving the precision of an initial document ranking wherein we utilize cluster information within a graph-based framework. The main idea is to perform re-ranking based on centrality within bipartite graphs of documents (on one side) and clusters (on the other side), on the premise that these are mutually reinforcing entities. Links between entities are created via consideration of language models induced from them. We find that our cluster-document graphs give rise to much better retrieval performance than previously proposed document-only graphs do. For example, authority-based re-ranking of documents via a HITS-style cluster-based approach outperforms a previously-proposed PageRank-inspired algorithm applied to solely-document graphs. Moreover, we also show that computing authority scores for clusters constitutes an effective method for identifying clusters containing a large percentage of relevant documents.

Simrank++: Query rewriting through link analysis of the click graph

Ioannis Antonellis — 2007-12-07T22:25:43-00:00

(29 October 2007)

We focus on the problem of query rewriting for sponsored search. We base rewrites on a historical click graph that records the ads that have been clicked on in response to past user queries. Given a query q, we first consider Simrank as a way to identify queries similar to q, i.e., queries whose ads a user may be interested in. We argue that Simrank fails to properly identify query similarities in our application, and we present two enhanced version of Simrank: one that exploits weights on click graph edges and another that exploits “evidence.” We experimentally evaluate our new schemes against Simrank, using actual click graphs and queries form Yahoo!, and using a variety of metrics. Our results show that the enhanced methods can yield more and better query rewrites.

Information Retrieval in Folksonomies: Search and Ranking

Andreas Hotho — 2007-04-13T09:53:28-00:00

: The Semantic Web: Research and Applications (2006), pp. 411-426.

Ranking Scientific Publications Using a Simple Model of Network Traffic

Dylan Walker — 2007-03-27T14:13:45-00:00

(13 Dec 2006)

To account for strong aging characteristics of citation networks, we modify Google's PageRank algorithm by initially distributing random surfers exponentially with age, in favor of more recent publications. The output of this algorithm, which we call CiteRank, is interpreted as approximate traffic to individual publications in a simple model of how researchers find new information. We develop an analytical understanding of traffic flow in terms of an RPA-like model and optimize parameters of our algorithm to achieve the best performance. The results are compared for two rather different citation networks: all American Physical Society publications and the set of high-energy physics theory (hep-th) preprints. Despite major differences between these two networks, we find that their optimal parameters for the CiteRank algorithm are remarkably similar.

Journal Status

Johan Bollen — 2007-03-20T03:58:55-00:00

(9 Jan 2006)

The status of an actor in a social context is commonly defined in terms of two factors: the total number of endorsements the actor receives from other actors and the prestige of the endorsing actors. These two factors indicate the distinction between popularity and expert appreciation of the actor, respectively. We refer to the former as popularity and to the latter as prestige. These notions of popularity and prestige also apply to the domain of scholarly assessment. The ISI Impact Factor (ISI IF) is defined as the mean number of citations a journal receives over a 2 year period. By merely counting the amount of citations and disregarding the prestige of the citing journals, the ISI IF is a metric of popularity, not of prestige. We demonstrate how a weighted version of the popular PageRank algorithm can be used to obtain a metric that reflects prestige. We contrast the rankings of journals according to their ISI IF and their weighted PageRank, and we provide an analysis that reveals both significant overlaps and differences. Furthermore, we introduce the Y-factor which is a simple combination of both the ISI IF and the weighted PageRank, and find that the resulting journal rankings correspond well to a general understanding of journal status.

Impact of Similarity Measures on Web-page Clustering

Alexander Strehl — 2005-03-28T20:53:22-00:00

(July 2000), pp. 58-64.

Clustering of web documents enables (semi-)automated categorization, and facilitates certain types of search. Any clustering method has to embed the documents in a suitable similarity space. While several clustering methods and the associated similarity measures have been proposed in the past, there is no systematic comparative study of the impact of similarity metrics on cluster quality, possibly because the popular cost criteria do not readily translate across qualitatively different metrics. ...

An index to quantify an individual's scientific output

JE Hirsch — 2005-08-08T10:17:12-00:00

(3 Aug 2005)

I propose the index $h$, defined as the number of papers with citation number higher or equal to $h$, as a useful index to characterize the scientific output of a researcher.

The anatomy of a large-scale hypertextual Web search engine

Sergey Brin — 2004-11-22T00:17:30-00:00

Link analysis ranking: algorithms, theory, and experiments

Allan Borodin — 2005-07-28T14:13:57-00:00

ACM Trans. Inter. Tech., Vol. 5, No. 1. (February 2005), pp. 231-297.

Inside PageRank

Monica Bianchini — 2005-04-26T09:15:45-00:00

ACM Trans. Inter. Tech., Vol. 5, No. 1. (February 2005), pp. 92-128.

Using Web structure for classifying and describing Web pages

Eric Glover — 2005-10-11T17:51:32-00:00

(2002)

The structure of the web is increasingly being used to improve organization, search, and analysis of information on the web. For example, Google uses the text in citing documents (documents that link to the target document) for search. We analyze the relative utility of document text, and the text in citing documents near the citation, for classification and description. Results show that the text in citing documents, when available, often has greater discriminative and descriptive power than...

How Popular is Your Paper? An Empirical Study of the Citation Distribution

S Redner — 2005-04-08T14:18:39-00:00

Eur. Phys. J., Vol. B, No. 4. (17 April 1998), pp. 131-138.

Numerical data for the distribution of citations are examined for: (i) papers published in 1981 in journals which are catalogued by the Institute for Scientific Information (783,339 papers) and (ii) 20 years of publications in Physical Review D, vols. 11-50 (24,296 papers). A Zipf plot of the number of citations to a given paper versus its citation rank appears to be consistent with a power-law dependence for leading rank papers, with exponent close to -1/2. This, in turn, suggests that the number of papers with x citations, N(x), has a large-x power law decay N(x)~x^-alpha, with alpha approximately equal to 3.

Finding authorities and hubs from link structures on the World Wide Web

Alan Borodin — 2005-10-19T02:34:24-00:00

(2001), pp. 415-429.

Recently, there have been a number of algorithms proposed for analyzing hypertext link structure so as to determine the best "authorities" for a given topic or query. While such analysis is usually combined with content analysis, there is a sense in which some algorithms are deemed to be "more balanced" and others "more focused".

Adaptive on-line page importance computation

Serge Abiteboul — 2005-06-30T10:33:26-00:00

(2003), pp. 280-290.

Mining the Web: Analysis of Hypertext and Semi Structured Data

Soumen Chakrabarti — 2005-03-02T15:59:19-00:00

(15 August 2002)

Mining the Web: Discovering Knowledge from Hypertext Data is the first book devoted entirely to techniques for producing knowledge from the vast body of unstructured Web data. Building on an initial survey of infrastructural issuesincluding Web crawling and indexingChakrabarti examines low-level machine learning techniques as they relate specifically to the challenges of Web mining. He then devotes the final part of the book to applications that unite infrastructure and analysis to bring machine learning to bear on systematically acquired and stored data. Here the focus is on results: the strengths and weaknesses of these applications, along with their potential as foundations for further progress. From Chakrabarti's workpainstaking, critical, and forward-lookingreaders will gain the theoretical and practical understanding they need to contribute to the Web mining effort. * A comprehensive, critical exploration of statistics-based attempts to make sense of Web Mining. * Details the special challenges associated with analyzing unstructured and semi-structured data. * Looks at how classical Information Retrieval techniques have been modified for use with Web data. * Focuses on today's dominant learning methods: clustering and classification, hyperlink analysis, and supervised and semi-supervised learning. * Analyzes current applications for resource discovery and social network analysis. * An excellent way to introduce students to especially vital applications of data mining and machine learning technology.</li></ul>