Forward search path count as an alternative indirect citation impact indicator

Jiang, Xiaorui; Zhuge, Hai

doi:10.1016/j.joi.2019.100977

Cited by 23 publications

(11 citation statements)

References 58 publications

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“…This narrow set of historically-significant papers has been the subject of several subsequent works, e.g., Ren et al (2018); Bornmann and Tekles (2020); Fontana et al (2020). A stream of prior works have indicated that these seminal works are better identified by global network-based metrics than by citation count (Mariani et al, 2016;Ren et al, 2018;Jiang and Zhuge, 2019;Wang et al, 2019;Ren, 2019;Xu et al, 2020), and better early detected by time-normalized metrics than by the raw metrics (Mariani et al, 2016;Dunaiski et al, 2019;Wang et al, 2019). Similar observations hold for the identification of expert-selected historically-significant patents (Mariani et al, 2019).…”

Section: Related Literaturementioning

confidence: 69%

The local structure of citation networks uncovers expert-selected milestone papers

Wang

Lü

2021

Journal of Informetrics

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Section: Related Literaturementioning

confidence: 69%

The local structure of citation networks uncovers expert-selected milestone papers

Wang

Lü

2021

Journal of Informetrics

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“…This gives us a feeling that the forward MPE method is more reliable than backward. This is possibly because the startpoints of forward main paths have a relatively higher correlation with high‐impact articles than backward main paths, because the meaning of forward search paths bears some resemblance to indirect citation influence if the citation network is reversed (Fragkiadaki & Evangelidis, ; Jiang, ). In addition, most key‐route paths are validated by either the forward or backward results and pass more connection points, which again signifies the superiority of the key‐route method.…”

Section: Methodsmentioning

confidence: 99%

Main path analysis on cyclic citation networks

Jiang

Zhu

Chen

2019

Asso for Info Science & Tech

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Main path analysis is a famous network‐based method for understanding the evolution of a scientific domain. Most existing methods have two steps, weighting citation arcs based on search path counting and exploring main paths in a greedy fashion, with the assumption that citation networks are acyclic. The only available proposal that avoids manual cycle removal is to preprint transform a cyclic network to an acyclic counterpart. Through a detailed discussion about the issues concerning this approach, especially deriving the “de‐preprinted” main paths for the original network, this article proposes an alternative solution with two‐fold contributions. Based on the argument that a publication cannot influence itself through a citation cycle, the SimSPC algorithm is proposed to weight citation arcs by counting simple search paths. A set of algorithms are further proposed for main path exploration and extraction directly from cyclic networks based on a novel data structure main path tree. The experiments on two cyclic citation networks demonstrate the usefulness of the alternative solution. In the meanwhile, experiments show that publications in strongly connected components may sit on the turning points of main path networks, which signifies the necessity of a systematic way of dealing with citation cycles.

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“…The second part contains the references of the Nobel Prize papers. This is inspired by the study where papers cited by popular textbooks and survey papers for a domain or certain topics were considered as gold standard (Jiang & Zhuge, 2019).…”

Section: Datasetsmentioning

confidence: 99%

Analysing academic paper ranking algorithms using test data and benchmarks: an investigation

et al. 2022

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Research on academic paper ranking has received great attention in recent years, and many algorithms have been proposed to automatically assess a large number of papers for this purpose. How to evaluate or analyse the performance of these ranking algorithms becomes an open research question. Theoretically, evaluation of an algorithm requires to compare its ranking result against a ground truth paper list. However, such ground truth does not exist in the field of scholarly ranking due to the fact that there does not and will not exist an absolutely unbiased, objective, and unified standard to formulate the impact of papers. Therefore, in practice researchers evaluate or analyse their proposed ranking algorithms by different methods, such as using domain expert decisions (test data) and comparing against predefined ranking benchmarks. The question is whether using different methods leads to different analysis results, and if so, how should we analyse the performance of the ranking algorithms? To answer these questions, this study compares among test data and different citation-based benchmarks by examining their relationships and assessing the effect of the method choices on their analysis results. The results of our experiments show that there does exist difference in analysis results when employing test data and different benchmarks, and relying exclusively on one benchmark or test data may bring inadequate analysis results. In addition, a guideline on how to conduct a comprehensive analysis using multiple benchmarks from different perspectives is summarised, which can help provide a systematic understanding and profile of the analysed algorithms.

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Forward search path count as an alternative indirect citation impact indicator

Cited by 23 publications

References 58 publications

The local structure of citation networks uncovers expert-selected milestone papers

The local structure of citation networks uncovers expert-selected milestone papers

Main path analysis on cyclic citation networks

Analysing academic paper ranking algorithms using test data and benchmarks: an investigation

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