Quantifying the impact of scholarly papers based on higher-order weighted citations

Quantifying the impact of a scholarly paper is of great significance, yet the effect of geographical distance of cited papers has not been explored. In this paper, we examine 30,596 papers published in Physical Review C, and identify the relationship between citations and geographical distances between author affiliations. Subsequently, a relative citation weight is applied to assess the impact of a scholarly paper. A higher-order weighted quantum PageRank algorithm is also developed to address the behavior of multiple step citation flow. Capturing the citation dynamics with higher-order dependencies reveals the actual impact of papers, including necessary self-citations that are sometimes excluded in prior studies. Quantum PageRank is utilized in this paper to help differentiating nodes whose PageRank values are identical.

[1]  S. Venkatramanan,et al.  Comprehensive studies of hydrogeochemical processes and quality status of groundwater with tools of cluster, grouping analysis, and fuzzy set method using GIS platform: a case study of Dalcheon in Ulsan City, Korea , 2015, Environmental Science and Pollution Research.

[2]  J. E. Hirsch,et al.  An index to quantify an individual's scientific research output , 2005, Proc. Natl. Acad. Sci. USA.

[3]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[4]  Richard S. J. Frackowiak,et al.  A functional neuroanatomy of hallucinations in schizophrenia , 1995, Nature.

[5]  David F. Gleich,et al.  PageRank beyond the Web , 2014, SIAM Rev..

[6]  E. Garfield.  The history and meaning of the journal impact factor. , 2006, JAMA.

[7]  Jiang Wu,et al.  Geographical knowledge diffusion and spatial diversity citation rank , 2012, Scientometrics.

[8]  Jasmine Novak,et al.  Geographic routing in social networks , 2005, Proc. Natl. Acad. Sci. USA.

[9]  Nitesh V. Chawla,et al.  Representing higher-order dependencies in networks , 2015, Science Advances.

[10]  Gustavo Lannelongue,et al.  Scholarly Impact Revisited , 2012 .

[11]  Yen-Liang Chen,et al.  An evolutionary PageRank approach for journal ranking with expert judgements , 2011, J. Inf. Sci..

[12]  L. Egghe,et al.  Theory and practise of the g-index , 2006, Scientometrics.

[13]  Feng Xia,et al.  Big Scholarly Data: A Survey , 2017, IEEE Transactions on Big Data.

[14]  Martin Rosvall,et al.  Robustness of journal rankings by network flows with different amounts of memory , 2014, J. Assoc. Inf. Sci. Technol..

[15]  Wolfgang Glänzel,et al.  Cross-national preference in co-authorship, references and citations , 2006, Scientometrics.

[16]  Yang Song,et al.  S-index: Towards Better Metrics for Quantifying Research Impact , 2015, ArXiv.

[17]  Feng Xia,et al.  Bibliographic Analysis of Nature Based on Twitter and Facebook Altmetrics Data , 2016, PloS one.

[18]  Ming Zeng,et al.  Ranking Scientific Articles by Exploiting Citations, Authors, Journals, and Time Information , 2013, AAAI.

[19]  Lise Getoor,et al.  FutureRank: Ranking Scientific Articles by Predicting their Future PageRank , 2009, SDM.

[20]  Feng Xia,et al.  An Overview on Evaluating and Predicting Scholarly Article Impact , 2017, Inf..

[21]  Feng Xia,et al.  Who are the rising stars in academia? , 2016, 2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL).

[22]  Xiaoming Zhang,et al.  Future Influence Ranking of Scientific Literature , 2014, SDM.

[23]  Jari Saramäki,et al.  The evolution of interdisciplinarity in physics research , 2012, Scientific Reports.

[24]  Feng Xia,et al.  Evaluating the Impact of Articles with Geographical Distances between Institutions , 2017, WWW.

[25]  Johannes Gehrke,et al.  Edge-Weighted Personalized PageRank: Breaking A Decade-Old Performance Barrier , 2015, KDD.

[26]  Weimao Ke,et al.  Mapping the diffusion of scholarly knowledge among major U.S. research institutions , 2006, Scientometrics.

[27]  Antonio Perianes-Rodríguez,et al.  Differences in citation impact across countries , 2015, J. Assoc. Inf. Sci. Technol..

[28]  Geoff Potvin,et al.  Beyond performance metrics: Examining a decrease in students’ physics self-efficacy through a social networks lens , 2016, 1809.01552.

[29]  Sergei Maslov,et al.  Finding scientific gems with Google's PageRank algorithm , 2006, J. Informetrics.

[30]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[31]  James A. Evans,et al.  Open Access and Global Participation in Science , 2009, Science.

[32]  Anthony E. Cawkell.  Checking research progress on 'image retrieval by shape-matching' using the Web of Science , 1998, Aslib Proc..

[33]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[34]  M. A. Martin-Delgado,et al.  Quantum Google algorithm , 2014 .

[35]  Vasiliki Kalavri,et al.  The shortest path is not always a straight line , 2016, Proc. VLDB Endow..

[36]  S. Ikhdair,et al.  Bound states of spatially dependent mass Dirac equation with the Eckart potential including Coulomb tensor interaction , 2014, 1401.7142.

[37]  Feng Xia,et al.  Identifying Anomalous Citations for Objective Evaluation of Scholarly Article Impact , 2016, PloS one.

[38]  David F. Gleich,et al.  Multilinear PageRank , 2014, SIAM J. Matrix Anal. Appl..

[39]  Vincent Larivière,et al.  Self-Selected or Mandated, Open Access Increases Citation Impact for Higher Quality Research , 2010, PloS one.

[40]  Martin Rosvall,et al.  Memory in network flows and its effects on spreading dynamics and community detection , 2013, Nature Communications.

[41]  George Karypis,et al.  Selective Markov models for predicting Web page accesses , 2004, TOIT.