Measure the Impact of Institution and Paper Via Institution-Citation Network

Bai, Xiaomei; Zhang, Fuli; Ni, Jin; Shi, Lei; Lee, Ivan

doi:10.1109/access.2020.2968459

Cited by 13 publications

(7 citation statements)

References 44 publications

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“…Zhang et al [58] elaborated on the process of author impact evaluation and discussed the author impact prediction models and common evaluation indicators. Bai et al [59] summarised and assessed the methods, indicators and risks from the perspectives of articles, scholars and journals. Wang et al [60] explored three knowledge flow patterns between citations, namely, the intensity of knowledge flow, knowledge dissemination ability and knowledge transfer capacity, to evaluate the impact of citations.…”

Section: Key-route Main Pathmentioning

confidence: 99%

Knowledge diffusion trajectories of PageRank: A main path analysis

Yan

2023

Journal of Information Science

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In the era of the Internet and big data, the PageRank (PR) algorithm is a constantly evolving research field. However, there is no systematic research to explore the overall development trend of the PR domain. This article evaluates 1446 articles related to the PR algorithm and provides a thorough understanding of the PR field through the main path analysis (MPA). Through two basic main paths, a number of papers that play a leading role have been identified, which outline the backbone of the PR domain. Based on the analysis of multiple main paths, four main subareas have been investigated. There are accelerating the computation of PR, comprehensive applications of PR, researches on academic impact assessment and age preference in network evolution. Finally, this article discusses the research findings and the future directions of the PR field. It is the first attempt to identify the development trend of the PR domain through MPA, thus providing an insight into the knowledge evolution of the PR field over the past two decades.

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Section: Key-route Main Pathmentioning

confidence: 99%

Knowledge diffusion trajectories of PageRank: A main path analysis

Yan

2023

Journal of Information Science

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“…To make full use of the information in academic networks and/or evaluate multiple entities at the same time, some researchers have proposed some PageRank variants by using various heterogeneous networks (Bai et al, 2020;Jiang et al, 2016;Liu et al, 2014;Meng & Kennedy, 2013;Yan et al, 2011;Yang et al, 2020;Yang et al, 2020;Zhang & Wu, 2018Zhang et al, , 2019aZhao et al, 2019;Zhou et al, 2021). Yan et al (2011) proposed an indicator, P-Rank, to score papers.…”

Section: Related Workmentioning

confidence: 99%

“…For balanced treatment of old and new papers, they considered both the publication age and recent citations of all the papers involved at the same time. Bai et al (2020) measured the impact of institutes and papers simultaneously based on the heterogeneous institution-citation network. Based on a heterogeneous network that including co-authorship, author-paper and paper citation relation, Zhou et al (2021) proposed an improved random walk algorithm to recommend research collaborators.…”

Section: Related Workmentioning

confidence: 99%

Measuring academic entities’ impact by content-based citation analysis in a heterogeneous academic network

Zhang

2021

Scientometrics

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Evaluating the impact of papers, researchers and venues objectively is of great significance to academia and beyond. This may help researchers, research organizations, and government agencies in various ways, such as helping researchers find valuable papers and authoritative venues and helping research organizations identify good researchers. A few studies find that rather than treating citations equally, differentiating them is a promising way for impact evaluation of academic entities. However, most of those methods are metadata-based only and do not consider contents of cited and citing papers; while a few content-based methods are not sophisticated, and further improvement is possible. In this paper, we study the citation relationships between entities by content-based approaches. Especially, an ensemble learning method is used to classify citations into different strength types, and a word-embedding based method is used to estimate topical similarity of the citing and cited papers. A heterogeneous network is constructed with the weighted citation links and several other features. Based on the heterogeneous network that consists of three types of entities, we apply an iterative PageRank-like method to rank the impact of papers, authors and venues at the same time through mutual reinforcement. Experiments are conducted on an ACL dataset, and the results demonstrate that our method greatly outperforms state-of-the art competitors in improving ranking effectiveness of papers, authors and venues, as well as in being robust against malicious manipulation of citations.

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“…The dataset comprises about 450, 000 articles from several APS journals, including Physical Review Letters, Physical Review A-E and Reviews of Modern Physics. This dataset has been largely used in several other studies [8,10,23,39]. The dataset comprises citations and additional paper metadata, including DOI, journal name, title, list of authors, affiliations, PACS code and others.…”

Section: A American Physical Society Datasetmentioning

confidence: 99%

A comparative analysis of local network similarity measurements: application to author citation networks

Vital,

Amancio

2021

Preprint

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Understanding the evolution of paper and author citations is of paramount importance for the design of research policies and evaluation criteria that can promote and accelerate scientific discoveries. Recently many studies on the evolution of science have been conducted in the context of the emergent science of science field. While many studies have probed the link problem in citation networks, only a few works have analyzed the temporal nature of link prediction in author citation networks. In this study we compared the performance of 10 well-known local network similarity measurements to predict future links in author citations networks. Differently from traditional link prediction methods, the temporal nature of the predict links is relevant for our approach. Our analysis revealed interesting results. The Jaccard coefficient was found to be among the most relevant measurements. The preferential attachment measurement, conversely, displayed the worst performance. We also found that the extension of local measurements to their weighted version do not significantly improved the performance of predicting citations. Finally, we also found that a neural network approach summarizing the information from all 10 considered similarity measurements was not able to provide the highest prediction performance.

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Measure the Impact of Institution and Paper Via Institution-Citation Network

Cited by 13 publications

References 44 publications

Knowledge diffusion trajectories of PageRank: A main path analysis

Knowledge diffusion trajectories of PageRank: A main path analysis

Measuring academic entities’ impact by content-based citation analysis in a heterogeneous academic network

A comparative analysis of local network similarity measurements: application to author citation networks

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