2,833 research outputs found
Applying weighted PageRank to author citation networks
This paper aims to identify whether different weighted PageRank algorithms
can be applied to author citation networks to measure the popularity and
prestige of a scholar from a citation perspective. Information Retrieval (IR)
was selected as a test field and data from 1956-2008 were collected from Web of
Science (WOS). Weighted PageRank with citation and publication as weighted
vectors were calculated on author citation networks. The results indicate that
both popularity rank and prestige rank were highly correlated with the weighted
PageRank. Principal Component Analysis (PCA) was conducted to detect
relationships among these different measures. For capturing prize winners
within the IR field, prestige rank outperformed all the other measures.Comment: 19 pages, 4 figures, 5 table
A multi-class approach for ranking graph nodes: models and experiments with incomplete data
After the phenomenal success of the PageRank algorithm, many researchers have
extended the PageRank approach to ranking graphs with richer structures beside
the simple linkage structure. In some scenarios we have to deal with
multi-parameters data where each node has additional features and there are
relationships between such features.
This paper stems from the need of a systematic approach when dealing with
multi-parameter data. We propose models and ranking algorithms which can be
used with little adjustments for a large variety of networks (bibliographic
data, patent data, twitter and social data, healthcare data). In this paper we
focus on several aspects which have not been addressed in the literature: (1)
we propose different models for ranking multi-parameters data and a class of
numerical algorithms for efficiently computing the ranking score of such
models, (2) by analyzing the stability and convergence properties of the
numerical schemes we tune a fast and stable technique for the ranking problem,
(3) we consider the issue of the robustness of our models when data are
incomplete. The comparison of the rank on the incomplete data with the rank on
the full structure shows that our models compute consistent rankings whose
correlation is up to 60% when just 10% of the links of the attributes are
maintained suggesting the suitability of our model also when the data are
incomplete
ArticleRank: a PageRank-based alternative to numbers of citations for analysing citation networks
Purpose - The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations.
Design/methodology/approach - ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets - a citation network based on an early paper on webometrics, and a self-citation network based on the 19 most cited papers in the Journal of Documentation - using citation data taken from the Web of Knowledge database.
Findings - ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited and ArticleRank is greatest for the most heavily cited articles in a dataset.
Originality/value - This is a novel application of the PageRank algorithm
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