Predictive ranking of computer scientists using CiteSeer data

Feitelson, Dror G.; Yovel, Uri

doi:10.1108/00220410410516644

Cited by 20 publications

(10 citation statements)

References 9 publications

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“…Chakrabarti and Agarwal (2006) use CiteSeer data in their experiments with learning ranking functions for real-world entity-relation graphs. Feitelson and Yovel (2004) have examined citation ranking lists obtained from CiteSeer and predicted future rankings of authors.…”

Section: Introductionmentioning

confidence: 99%

Mining citation information from CiteSeer data

Fiala

2010

Scientometrics

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Abstract:The CiteSeer digital library is a useful source of bibliographic information. It allows for retrieving citations, co-authorships, addresses, and affiliations of authors and publications. In spite of this, it has been relatively rarely used for automated citation analyses. This article describes our findings after extensively mining from the CiteSeer data. We explored citations between authors and determined rankings of influential scientists using various evaluation methods including citation and in-degree counts, HITS, PageRank, and its variations based on both the citation and collaboration graphs. We compare the resulting rankings with lists of computer science award winners and find out that award recipients are almost always ranked high. We conclude that CiteSeer is a valuable, yet not fully appreciated, repository of citation data and is appropriate for testing novel bibliometric methods.

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Section: Introductionmentioning

confidence: 99%

Mining citation information from CiteSeer data

Fiala

2010

Scientometrics

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“…Informetric analyses on research fronts are well established (e.g., Garfield, 1994;Morris, Yen, Wu, & Asnake, 2003;Persson, 1994;Price, 1965); in addition, there have been attempts at predicting the ranking of papers and authors by analyzing research fronts (e.g., Burrell, 2003;Feitelson & Yovel, 2003;Glänzel, 1997;Glänzel & Schoepflin, 1994;Glänzel & Schubert, 1995). Furthermore, there are also examples of studies of research fields where analyses of trends have been used to predict future development of research fields (e.g., Yashin & Yashin, 2001).…”

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confidence: 99%

Changes in the LIS research front: Time‐sliced cocitation analyses of LIS journal articles, 1990–2004

Åström

2007

J. Am. Soc. Inf. Sci.

125

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Based on articles published in 1990-2004 in 21 libraryand information science (LIS) journals, a set of cocitation analyses was performed to study changes in research fronts over the last 15 years, where LIS is at now, and to discuss where it is heading. To study research fronts, here defined as current and influential cocited articles, a citations among documents methodology was applied; and to study changes, the analyses were time-sliced into three 5-year periods. The results show a stable structure of two distinct research fields: informetrics and information seeking and retrieval (ISR). However, experimental retrieval research and user oriented research have merged into one ISR field; and IR and informetrics also show signs of coming closer together, sharing research interests and methodologies, making informetrics research more visible in mainstream LIS research. Furthermore, the focus on the Internet, both in ISR research and in informetrics-where webometrics quickly has become a dominating research area-is an important change. The future is discussed in terms of LIS dependency on technology, how integration of research areas as well as technical systems can be expected to continue to characterize LIS research, and how webometrics will continue to develop and find applications.

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“…We found only 12 articles that discuss citation prediction in the sciences. Notably the consensus in this literature is that predicting citations is extremely difficult if not impossible due to various discussed reasons (Getoor 2003;Rattigan and Jensen 2003;Feitelson and Yovel 2004). Lokker et al (2008) recently presented a moderately predictive regression model for predicting citation counts 2 years after publication.…”

Section: Introductionmentioning

confidence: 98%

Using content-based and bibliometric features for machine learning models to predict citation counts in the biomedical literature

Aliferis

2010

Scientometrics

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The most popular method for judging the impact of biomedical articles is citation count which is the number of citations received. The most significant limitation of citation count is that it cannot evaluate articles at the time of publication since citations accumulate over time. This work presents computer models that accurately predict citation counts of biomedical publications within a deep horizon of 10 years using only predictive information available at publication time. Our experiments show that it is indeed feasible to accurately predict future citation counts with a mixture of content-based and bibliometric features using machine learning methods. The models pave the way for practical prediction of the long-term impact of publication, and their statistical analysis provides greater insight into citation behavior.

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Predictive ranking of computer scientists using CiteSeer data

Cited by 20 publications

References 9 publications

Mining citation information from CiteSeer data

Mining citation information from CiteSeer data

Changes in the LIS research front: Time‐sliced cocitation analyses of LIS journal articles, 1990–2004

Using content-based and bibliometric features for machine learning models to predict citation counts in the biomedical literature

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