Time-Aware Ranking in Dynamic Citation Networks

Ghosh, Rumi; Kuo, Tsung-Ting; Hsu, Chun‐Nan; Lin, Shou-De; Lerman, Kristina

doi:10.1109/icdmw.2011.183

Cited by 35 publications

(47 citation statements)

References 24 publications

(37 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The tool returns for a given article the identifiers (pmids/pmcids) of all articles that cite, or are cited by it. Two citation-based impact measures are calculated on the constructed network: the PageRank [5] and the RAM scores [6]. These two measures were selected based on the results of a recent experimental study [7], which found them to perform best in capturing the overall and the current impact of an article (i.e., its "influence" and its "popularity"), respectively .…”

Section: Calculation Of Citation-based Measuresmentioning

confidence: 99%

See 1 more Smart Citation

BIP4COVID19: Releasing impact measures for articles relevant to COVID-19

Vergoulis

Kanellos

Chatzopoulos

et al. 2020

Preprint

View full text Add to dashboard Cite

Since the beginning of the 2019-20 coronavirus pandemic, a large number of relevant articles has been published or become available in preprint servers. These articles, along with earlier related literature, compose a valuable knowledge base affecting contemporary research studies or, even, government actions to limit the spread of the disease and treatment decisions taken by physicians. However, the number of such articles is increasing at an intense rate making the exploration of the relevant literature and the identification of useful knowledge in it challenging. In this work, we describe BIP4COVID19, an open dataset compiled to facilitate the coronavirus-related literature exploration, by providing various indicators of scientific impact for the relevant articles. Finally, we provide a publicly accessible web interface on top of our data, allowing the exploration of the publications based on the computed indicators.

show abstract

Section: Calculation Of Citation-based Measuresmentioning

confidence: 99%

“…The same holds for the tweet counting process 7 . For the calculation of popularity (RAM [6]) and influence (PageRank [5]) scores, the open PaperRanking [7] library 8 was used. Finally, retrieving Twitter objects based on given tweet IDs was done using the twarc Python library 9 .…”

Section: Code Availabilitymentioning

confidence: 99%

BIP4COVID19: Releasing impact measures for articles relevant to COVID-19

Vergoulis

Kanellos

Chatzopoulos

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…To assess the importance of papers, graph ranking algorithms such as PageRank and its variants have been applied [10,20,21,22]. In [10], the authors further take time into consideration in order to overcome the recency bias that favors "old" papers. Apart from this, graph clustering is investigated to identify meaningful topics, such as [5,8,18,19].…”

Section: Related Workmentioning

confidence: 99%

“…Even sophisticated citation-based measurement, e.g., [10,20,21,22], without taking into account of topics, can lead to bad judgement: a well recognized theoretic paper about graphic model in "Bayes learning" might receive less credit in "data engineering" and "very large database" due to the computational difficulty that limits its application.…”

Section: Topic Milestone Papersmentioning

confidence: 99%

Understanding evolution of research themes

Wang

Zhai

Roth

2013

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

View full text Add to dashboard Cite

Understanding how research themes evolve over time in a research community is useful in many ways (e.g., revealing important milestones and discovering emerging major research trends). In this paper, we propose a novel way of analyzing literature citation to explore the research topics and the theme evolution by modeling article citation relations with a probabilistic generative model. The key idea is to represent a research paper by a "bag of citations" and model such a "citation document" with a probabilistic topic model. We explore the extension of a particular topic model, i.e., Latent Dirichlet Allocation (LDA), for citation analysis, and show that such a Citation-LDA can facilitate discovering of individual research topics as well as the theme evolution from multiple related topics, both of which in turn lead to the construction of evolution graphs for characterizing research themes. We test the proposed citation-LDA on two datasets: the ACL Anthology Network (AAN) of natural language research literatures and PubMed Central (PMC) archive of biomedical and life sciences literatures, and demonstrate that Citation-LDA can effectively discover the evolution of research themes, with better formed topics than (conventional) Content-LDA.

show abstract

“…Beyond simple citations count, researchers have explored methods that analyze the structure of citation networks to identify important papers [31], [32] or predict which papers will be important in the future [33].…”

Section: Related Workmentioning

confidence: 99%

Identifying Transformative Scientific Research

Huang

Hsu²,

Lerman³

2013

2013 IEEE 13th International Conference on Data Mining

Self Cite

View full text Add to dashboard Cite

Abstract-Transformative research refers to research that shifts or disrupts established scientific paradigms. Notable examples include the discovery of high-temperature superconductivity that disrupted the theory established 30 years ago. Identifying potential transformative research early and accurately is important for funding agencies to maximize the impact of their investments. It also helps scientists identify and focus their attention on promising emerging works. This paper presents a datadriven approach where citation patterns of scientific papers are analyzed to quantify how much a potential challenger idea shifts an established paradigm. The key idea is that transformative research creates an observable disruption in the structure of "information cascades," chains of references that can be traced back to the papers establishing some scientific paradigm. Such a disruption is visible soon after the challenger's introduction. We define a disruption score to quantify the disruption and develop an algorithm to compute it from a large citation network. Experimental results show that our approach can successfully identify transformative scientific papers that disrupt established paradigms in Physics and Computer Science, regardless of whether the challenger paradigm is an instant hit or a classic whose contribution is formally recognized with a Nobel Prize decades later.

show abstract

Time-Aware Ranking in Dynamic Citation Networks

Cited by 35 publications

References 24 publications

BIP4COVID19: Releasing impact measures for articles relevant to COVID-19

BIP4COVID19: Releasing impact measures for articles relevant to COVID-19

Understanding evolution of research themes

Identifying Transformative Scientific Research

Contact Info

Product

Resources

About