Characterizing and Mining the Citation Graph of the Computer Science Literature

An, Yuan; Janssen, Jeannette; Milios, Evangelos

doi:10.1007/s10115-003-0128-3

Cited by 61 publications

(45 citation statements)

References 14 publications

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“…On the other hand, Hopcroft et al (2004) track evolving communities in the whole CiteSeer paper citation graph. An et al (2004) have constructed article citation graphs in several research domains by querying CiteSeer and have explored them in terms of components. Popescul et al (2003) have classified CiteSeer articles into categories by venues.…”

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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“…The second graph is a citation network, obtained from crawling citeseer [1]. The final dataset is a co-authorship [7] network, which is obtained from DBLP [3]. The final group of inputs are road networks taken from the DIMACS homepage [18].…”

Section: Methodsmentioning

confidence: 99%

Orca Reduction and ContrAction Graph Clustering

Delling

Görke

Schulz

et al. 2009

Algorithmic Aspects in Information and Management

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Abstract. During the last years, a wide range of huge networks has been made available to researchers. The discovery of natural groups, a task called graph clustering, in such datasets is a challenge arising in many applications such as the analysis of neural, social, and communication networks. We here present Orca, a new graph clustering algorithm, which operates locally and hierarchically contracts the input. In contrast to most existing graph clustering algorithms, which operate globally, Orca is able to cluster inputs with hundreds of millions of edges in less than 2.5 hours, identifying clusterings with measurably high quality. Our approach explicitly avoids maximizing any single index value such as modularity, but instead relies on simple and sound structural operations. We present and discuss the Orca algorithm and evaluate its performance with respect to both clustering quality and running time, compared to other graph clustering algorithms.

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“…We note that citations in scientific research in general [20] and computer science in particular [3,17], are distributed according to a zipf curve, with α = 1/2, at least for the top 10-20% of the total universe of published research papers. (The rest are write once, read none.)…”

Section: Zipfmentioning

confidence: 99%

Conference reviewing considered harmful

Anderson

2009

SIGOPS Oper. Syst. Rev.

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This paper develops a model of computer systems research to help prospective authors understand the often obscure workings of conference program committees. We present data to show that the variability between reviewers is often the dominant factor as to whether a paper is accepted. We argue that paper merit is likely to be zipf distributed, making it inherently difficult for program committees to distinguish between most papers. We use game theory to show that with noisy reviews and zipf merit, authors have an incentive to submit papers too early and too often. These factors make conference reviewing, and systems research as a whole, less efficient and less effective. We describe some recent changes in conference design to address these issues, and we suggest some further potential improvements.

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Characterizing and Mining the Citation Graph of the Computer Science Literature

Cited by 61 publications

References 14 publications

Mining citation information from CiteSeer data

Mining citation information from CiteSeer data

Orca Reduction and ContrAction Graph Clustering

Conference reviewing considered harmful

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