1
|
Zhang Y, Wang M, Zipperle M, Abbasi A, Tani M. RelRank: A relevance-based author ranking algorithm for individual publication venues. Inf Process Manag 2023. [DOI: 10.1016/j.ipm.2022.103156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
2
|
Impact of h-index on author’s rankings: an improvement to the h-index for lower-ranked authors. Scientometrics 2022. [DOI: 10.1007/s11192-022-04464-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
|
3
|
Bai X, Zhang F, Li J, Xu Z, Patoli Z, Lee I. Quantifying scientific collaboration impact by exploiting collaboration-citation network. Scientometrics 2021. [DOI: 10.1007/s11192-021-04078-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
4
|
Zhang Y, Wang M, Saberi M, Chang E. Knowledge fusion through academic articles: a survey of definitions, techniques, applications and challenges. Scientometrics 2020. [DOI: 10.1007/s11192-020-03683-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
|
5
|
Kong X, Mao M, Jiang H, Yu S, Wan L. How does collaboration affect researchers’ positions in co-authorship networks? J Informetr 2019. [DOI: 10.1016/j.joi.2019.07.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|
6
|
|
7
|
Tidke B, Mehta R, Dhanani J. Multimodal ensemble approach to identify and rank top-k influential nodes of scholarly literature using Twitter network. J Inf Sci 2019. [DOI: 10.1177/0165551519837190] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Scholarly literature is an immense network of activities, linked via collaborations or information propagation. Analysing such network can be leveraged by harnessing rich semantic meaning of scholarly graph. Identifying and ranking top- k influential nodes from various domains of scholarly literature using social media data are still infancy. Social networking sites like Twitter provide an opportunity to create inventive graph-based measures to identify and rank influential nodes such as scholars, articles, journal, information spreading media and academic institutions of scholarly literature. Many network-based models such as centrality measures have been proposed to identify influential nodes. The empirical annotation shows that centrality measures for finding influential nodes are high in computational complexity. In addition, notion of these measures have high variance, which signifies an influential node deviation with change in application and nature of information flows in the network. The research aims to propose an ensemble learning approach based on multimodal majority voting influence (MMMVI) to identify and weighted multimodal ensemble average influence (WMMEAI) to rank top- k influential nodes in Twitter network data set of well-known three influential nodes, that is, academic institution, scholar and journal. The empirical analysis has been accomplished to learn practicability and efficiency of the proposed approaches when compared with state-of-the-art approaches. The experimental result shows that the ensemble approach using surface learning models (SLMs) can lead to better identification and ranking of influential nodes with low computational complexity.
Collapse
|
8
|
Research Front Detection and Topic Evolution Based on Topological Structure and the PageRank Algorithm. Symmetry (Basel) 2019. [DOI: 10.3390/sym11030310] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Research front detection and topic evolution has for a long time been an important direction for research in the informetrics field. However, most previous studies either simply use a citation count for scientific document clustering or assume that each scientific document has the same importance in detecting the clustering theme in a cluster. In this study, utilizing the topological structure and the PageRank algorithm, we propose a new research front detection and topic evolution approach based on graph theory. This approach is made up of three stages: (1) Setting a time window with appropriate length according to the accuracy of scientific documents clustering results and the time delay of a scientific document to be cited, dividing scientific documents into several time windows according to their years of publication, calculating similarities between them according to their topological structure, and clustering them in each time window based on the fast greedy algorithm; (2) combining the PageRank algorithm and keywords’ frequency to detect the clustering theme, which assumes that the more important a scientific document in the cluster is, the greater the possibility that it is cited by the other documents in the same cluster; and (3) reconstructing the cluster graph where nodes represent clusters and edges’ strengths represent the similarities between different clusters, then detecting research front and identifying topic evolution based on the reconstructed cluster graph. To evaluate the performance of our proposed approach, the scientific documents related to data mining and covered by Science Citation Index Expanded (SCI-EXPANDED) or Social Science Citation Index (SSCI) in Web of Science are collected as a case study. The experiment’s results show that the proposed approach can obtain reasonable clustering results, and it is effective for research front detection and topic evolution.
Collapse
|
9
|
Dunaiski M, Geldenhuys J, Visser W. Globalised vs averaged: Bias and ranking performance on the author level. J Informetr 2019. [DOI: 10.1016/j.joi.2019.01.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
|
10
|
|
11
|
Dunaiski M, Geldenhuys J, Visser W. How to evaluate rankings of academic entities using test data. J Informetr 2018. [DOI: 10.1016/j.joi.2018.06.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
12
|
|
13
|
Fiala D, Tutoky G. PageRank-based prediction of award-winning researchers and the impact of citations. J Informetr 2017. [DOI: 10.1016/j.joi.2017.09.008] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
14
|
|
15
|
A multiple-link, mutually reinforced journal-ranking model to measure the prestige of journals. Scientometrics 2017. [DOI: 10.1007/s11192-017-2262-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
16
|
Silva FN, Amancio DR, Bardosova M, Costa LDF, Oliveira ON. Using network science and text analytics to produce surveys in a scientific topic. J Informetr 2016. [DOI: 10.1016/j.joi.2016.03.008] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|
17
|
|