ALIS: A novel metric in lineage-independent evaluation of scholars

2021 ◽  
pp. 016555152110391
Author(s):  
Sudeepa Roy Dey ◽  
Archana Mathur ◽  
B.S Dayasagar ◽  
Snehanshu Saha
Keyword(s):  
Web Of Science ◽  
Real Data ◽  
Graph Structure ◽  
Proof Of Concept ◽  
Simulated Sample ◽  
Independent Evaluation ◽  
Funding Agencies ◽  
Scholarly Influence ◽  
Optimisation Model ◽  
Academic Genealogy

Evaluative bibliometrics often attempts to explore various methods to measure individual scholarly influence. Scholarly independence (SI) is a unique indicator that can be used to understand and assess the research performances of individual scholars. The SI is a rare quality that most funding agencies and universities seek during funding decisions or hiring processes. We propose author lineage independent score (ALIS), a unique model to measure SI of a scholar by using his or her academic genealogy tree as the underlying graph structure. The analysis is performed on real data of 100 authors, collected from the Web of Science (WoS) and the Mathematics Genealogy Project. The analysis is further validated on a larger scale, on a simulated sample of 10,000 authors. The simulation exercise is the proof-of-concept for scalability of the metric and the proposed optimisation model. ALIS exploits genealogical relationships between scholars and their mentors and collaborating communities and constructs an influence scoring model based on the Genealogy tree structure of the respective scholars. The implications from the theoretical model are found to be profound in tracing known and recursive citation patterns among peers. The genealogy tree is used to investigate the advisor–advisee relationship and lays the foundation for defining metrics used to calculate the various indicators such as non-genealogy citations (NGCs), non-community citations (NCCs) and other citation quotient (OCQ). As these indicators/parameters are novel and thus not readily accessible, algorithms are written to compute these indicator values for the scholars under study.

Who is who in library and information science research? The integrative application of scholarly influence indicators

2020 ◽  
Vol 52 (4) ◽  
pp. 1186-1196
Author(s):  
Reza Mokhtarpour ◽  
Ali Akbar Khasseh
Keyword(s):  
Information Science ◽  
Web Of Science ◽  
Science Research ◽  
Decision Makers ◽  
Intellectual Structure ◽  
Library And Information Science ◽  
Scientometric Indicators ◽  
Scholarly Influence

This research concerns determining authors’ scientific influence in library and information science research and their impact on the intellectual structure of the discipline by means of integrative indicators of the Scholarly Capital Model and co-authorship patterns. Research records comprised articles published from 1945 to 2016 in library and information science core journals and indexed in Web of Science. CiteSpace (software for visualization of scientific patterns and trends) was employed to map the intellectual structure of library and information science research based on co-authorship patterns. The results showed that the top 10 authors of library and information science research with the highest scores in terms of influence indicators (except for one person) were mostly concerned with the field of scientometrics which can be considered as the special impact of scientometric authors on the intellectual structure of library and information science research especially in recent years. Based on the results of the research, integrative use of scientometric indicators for measuring authors’ level of scholarly influence may grant a more precise perspective for decision makers in the field of library and information science.

scholarly journals A medical network clustering method with weighted graph structure

2020 ◽  
pp. 002029402095246
Author(s):  
Hong Wu ◽  
Zijian Fu ◽  
Yizhou Wang
Keyword(s):  
Clustering Algorithm ◽  
Weighted Graph ◽  
Real Data ◽  
Diffusion Method ◽  
Network Clustering ◽  
Graph Structure ◽  
Network Graph ◽  
Analysis Algorithm ◽  
Network Function ◽  
Medical Network

Today, most of the databases used for drug information mining are derived from the collection of many treatments under a single disease, and some special drug compatibility rules can be found from them. However, researchers’ exploration of medical data is not limited to this. The comparative analysis of drugs for different diseases has become a new research point. In this paper, the drug is used as a node, the relationship is the edge connecting the two nodes, the co-occurrence frequency of the drug is used as the weight of the edge to establish a network graph. We use the clustering algorithm of the weighted network graph center diffusion method combining the network topology and the edge weights to divide the network graph into communities. Then we proposed the Structural Clustering Algorithm on Weighted Networks (SCW), it helps to study the prescription of medical prescriptions and provides more scientific recommendations for auxiliary prescriptions. In the experiment, SCW is compared with the classic community discovery algorithm CPM, the network function modular analysis algorithm MCODE and the hierarchical network graph structure analysis algorithm BGLL. We analyze the results according to NMI, ARI and F-Measure. Finally, a case study of real data was conducted to ensure the correctness and effectiveness of the algorithm, and to obtain the potential drug combination in the medical prescription.

scholarly journals Curcuma Longa (Medicinal Plant) Research: A Scientometric Assessment of Global Publications Output with Reference to Web of Science

2021 ◽  
Vol 12 (5) ◽  
pp. 1477-1496
Author(s):  
Dr.K. Sivasekaran, Et. al.
Keyword(s):  
Web Of Science ◽  
Citation Index ◽  
Curcuma Longa ◽  
Total Duration ◽  
Public Library ◽  
United States Department ◽  
Food Chemistry ◽  
Entire Study ◽  
Research Activities ◽  
Funding Agencies

The present study explores the characteristics of publication records for a total duration of twenty years, from 2000 to 2019, in the field of Curcuma longa research. This study has been carried out based on the multidisciplinary bibliographic database available with the Web of Science in Science Citation Index-Expanded (SCIE) and Social Sciences Citation Index (SSCI) and its implications, using the means of scientometrics research techniques. In order to make this analysis a holistic and comprehensive survey of the research trends in the chosen field, the following variables are taken into account: growth rate; global citation scores; distribution of publications by journals, conferences and institutions; favored media of communication; Hirsch index and citation profile of top institutions, countries and authors; contribution of funding agencies; high number of cited papers and characteristics of their bibliographic details. The total number of publication records has been found out to be 6087 during the study period. These 6087 publications have received 171 h-index, 1, 84,715 global citations score and 30.34 average citations. On the whole, 6087 records were published during the study period (2000-2019) in 18 types of documents from 107 countries with 2005 journals, contributed by as many as 20855 authors affiliated to 4879 institutions. These publications were brought out in 18 languages, and they received 1, 56,986 cited references. Majority of the records were in the form of journal articles, reviews, papers in conference proceedings and meeting abstracts, accounting for 97 percent of the total publications. Naturally enough, English happens to be the leading language of 98.8 percent to have accounted for the most number of publications. The four largest contributing countries in the total literature on Curcuma longa during the entire study period are India (24.68 percent), USA (17.7 percent), China (12.2 percent) and Iran (6.09 percent) respectively. The largest institutional contributor of publication records happens to be the Mashhad University of Medical Sciences, Mashhad, Iran with 1.8 percent of the papers to its credit. The most prolific authors to have published more number of research documents during the study period were Sahebkar A (73 papers), Aggarwal BB (67 papers), Nayak S (35 papers) and Kumar A (33 papers). The journal of “Food chemistry” Elsevier ltd tops the list of journals with maximum number of publication records in the field for the given study period with 70 publications, followed by “Journal of Agricultural and Food Chemistry” American Chemical Society (69 papers), “Phytotherapy Research” John Wiley and sons Ltd (63 papers) and “PLOS One” Public Library of Science (59 papers). While the Third World Congress on Medicinal and Aromatic Plants - WOCMAP III held in February 2003 at Thailand resulted in the publication of 6 papers, the following three major funding agencies contributed immensely to the research activities in the field: ‘National Natural Science Foundation of China’ with 318papers, United States Department of Health & Human Services, USA with 304 papers and Council of Scientific Industrial Research, India with 99 papers.

scholarly journals Analysis of publications on paratuberculosis from 1995 to 2009 with emphasis on the period from 2005 to 2009

2010 ◽  
Vol 55 (No. 2) ◽  
pp. 43-54 ◽  
Author(s):  
M. Kaevska ◽  
K. Hruska
Keyword(s):  
Inflammatory Bowel Disease ◽  
Crohn’S Disease ◽  
Mycobacterium Avium ◽  
Bowel Disease ◽  
Web Of Science ◽  
Descriptive Data ◽  
Funding Agencies ◽  
Inflammatory Bowel ◽  
Number Of Publications ◽  
The Web

The importance of paratuberculosis, an infectious bowel disease of ruminants, and Crohn’s disease, a type of inflammatory bowel disease in humans with suspected links with <I>Mycobacterium avium</I> subsp. <I>paratuberculosis</I>, is evident from the steadily increasing number of publications on these topics. Data from the Web of Science databases were analysed by authors, institutions, countries and funding agencies, involved in research. A summary of the descriptive data for the most frequently cited publications are presented here.

scholarly journals Bayesian Topology Learning and noise removal from network data

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Mahmoud Ramezani Mayiami ◽  
Mohammad Hajimirsadeghi ◽  
Karl Skretting ◽  
Xiaowen Dong ◽  
Rick S. Blum ◽  
...  
Keyword(s):  
Real Data ◽  
Noise Removal ◽  
Proximal Point Method ◽  
Graph Structure ◽  
Analysis Model ◽  
Proximal Point ◽  
Factor Analysis Model ◽  
Graph Topology ◽  
Underlying Graph ◽  
Industrial Iot

AbstractLearning the topology of a graph from available data is of great interest in many emerging applications. Some examples are social networks, internet of things networks (intelligent IoT and industrial IoT), biological connection networks, sensor networks and traffic network patterns. In this paper, a graph topology inference approach is proposed to learn the underlying graph structure from a given set of noisy multi-variate observations, which are modeled as graph signals generated from a Gaussian Markov Random Field (GMRF) process. A factor analysis model is applied to represent the graph signals in a latent space where the basis is related to the underlying graph structure. An optimal graph filter is also developed to recover the graph signals from noisy observations. In the final step, an optimization problem is proposed to learn the underlying graph topology from the recovered signals. Moreover, a fast algorithm employing the proximal point method has been proposed to solve the problem efficiently. Experimental results employing both synthetic and real data show the effectiveness of the proposed method in recovering the signals and inferring the underlying graph.

Supporting successful data sharing practices in earthquake engineering

2019 ◽  
Vol 37 (4) ◽  
pp. 764-780
Author(s):  
Shuheng Wu ◽  
Adam Worrall
Keyword(s):  
Data Sharing ◽  
Earthquake Engineering ◽  
Design Methodology ◽  
Structured Interviews ◽  
Data Repositories ◽  
Content Type ◽  
Funding Agencies ◽  
Data Ownership ◽  
Project Size ◽  
Academic Genealogy

Purpose Prior studies identified a need for further comparison of data-sharing practices across different disciplines and communities. Toward addressing this need, the purpose of this paper is to examine the data-sharing practices of the earthquake engineering (EE) community, which could help inform data-sharing policies in EE and provide different stakeholders of the EE community with suggestions regarding data management and curation. Design/methodology/approach This study conducted qualitative semi-structured interviews with 16 EE researchers to gain an understanding of which data might be shared, with whom, under what conditions and why; and their perceptions of data ownership. Findings This study identified 29 data-sharing factors categorized into five groups. Requirements from funding agencies and academic genealogy were frequent impacts on EE researchers’ data-sharing practices. EE researchers were uncertain of data ownership and their perceptions varied. Originality/value Based on the findings, this study provides funding agencies, research institutions, data repositories and other stakeholders of the EE community with suggestions, such as allowing researchers to adjust the timeframe they can withhold data based on project size and the amount of experimental data generated; expanding the types and states of data required to share; defining data ownership in grant requirements; integrating data sharing and curation into curriculum; and collaborating with library and information schools for curriculum development.

scholarly journals Solving #SAT and MAXSAT by Dynamic Programming

2015 ◽  
Vol 54 ◽  
pp. 59-82 ◽  
Author(s):  
Sigve Hortemo Sæther ◽  
Jan Arne Telle ◽  
Martin Vatshelle
Keyword(s):  
Dynamic Programming ◽  
Constraint Satisfaction Problems ◽  
Structure Theory ◽  
Graph Structure ◽  
Proof Of Concept ◽  
Incidence Graph ◽  
Model Counting ◽  
Structural Decompositions ◽  
Programming Algorithms ◽  
Input Formula

We look at dynamic programming algorithms for propositional model counting, also called #SAT, and MaxSAT. Tools from graph structure theory, in particular treewidth, have been used to successfully identify tractable cases in many subfields of AI, including SAT, Constraint Satisfaction Problems (CSP), Bayesian reasoning, and planning. In this paper we attack #SAT and MaxSAT using similar, but more modern, graph structure tools. The tractable cases will include formulas whose class of incidence graphs have not only unbounded treewidth but also unbounded clique-width. We show that our algorithms extend all previous results for MaxSAT and #SAT achieved by dynamic programming along structural decompositions of the incidence graph of the input formula. We present some limited experimental results, comparing implementations of our algorithms to state-of-the-art #SAT and MaxSAT solvers, as a proof of concept that warrants further research.

scholarly journals Bibliometric Analysis of digitization business economy

2020 ◽  
Vol 214 ◽  
pp. 03016
Author(s):  
Chung-Lien Pan ◽  
JiaRong Lin ◽  
YiHua Wang ◽  
ZhiXiang Zhou ◽  
YiJiao MO
Keyword(s):  
Information Science ◽  
Web Of Science ◽  
The United States ◽  
Sharing Economy ◽  
Library And Information Science ◽  
Business Economics ◽  
Funding Agencies ◽  
The Subject ◽  
Ict Industry ◽  
Industry Professionals

Digital technology is being applied by different organizations, for example, the sharing economy, blockchain, and other topics are very popular in recent years, which leads to obvious changes in different fields. At the same time, there is growing interest in the digital business economy. However, there is less bibliometrics on the subject, and this is a good solution for addressing the opportunities and risks of digital transformation. Based on 731 articles retrieved from the Web of Science（Wos） database between 2000 and 2020, the study reviewed the literature on “ICT industry”, “digital economy”, “economic analysis” and “market research”. Since 2015, publications have experienced rapid growth in several disciplines, such as management, business, economics, library and information science, business economics, etc. At the same time, research institutions in Germany, the United States, and Sweden have performed well in this field. Using this database, the author analyzed what happened and made a concise keyword map to clearly show the connections among the topics based on the co-occurrence network generated by the keyword data. This paper provides a reference point for researchers, funding agencies, policymakers, and industry professionals to study the progress of the digital business economy.

scholarly journals Network dependence testing via diffusion maps and distance-based correlations

Biometrika ◽  
2019 ◽  
Vol 106 (4) ◽  
pp. 857-873 ◽  
Author(s):  
Youjin Lee ◽  
Cencheng Shen ◽  
Carey E Priebe ◽  
Joshua T Vogelstein
Keyword(s):  
Network Connectivity ◽  
Graph Embedding ◽  
Real Data ◽  
Graph Structure ◽  
Diffusion Maps ◽  
Test Statistic ◽  
Dependency Structure ◽  
The Core ◽  
Empirical Performance ◽  
Network Dependence

Summary Deciphering the associations between network connectivity and nodal attributes is one of the core problems in network science. The dependency structure and high dimensionality of networks pose unique challenges to traditional dependency tests in terms of theoretical guarantees and empirical performance. We propose an approach to test network dependence via diffusion maps and distance-based correlations. We prove that the new method yields a consistent test statistic under mild distributional assumptions on the graph structure, and demonstrate that it is able to efficiently identify the most informative graph embedding with respect to the diffusion time. The methodology is illustrated on both simulated and real data.

scholarly journals Advances in constraining intrinsic alignment models with hydrodynamic simulations

2021 ◽  
Vol 508 (1) ◽  
pp. 637-664 ◽  
Author(s):  
S Samuroff ◽  
R Mandelbaum ◽  
J Blazek
Keyword(s):  
Large Scale ◽  
Real Data ◽  
Model Parameters ◽  
Proof Of Concept ◽  
Hydrodynamic Simulations ◽  
Data Vector ◽  
Galaxy Type ◽  
Zero Values ◽  
Novel Method ◽  
Alignment Model

ABSTRACT We use galaxies from the illustristng, massiveblack-ii, and illustris-1 hydrodynamic simulations to investigate the behaviour of large scale galaxy intrinsic alignments. Our analysis spans four redshift slices over the approximate range of contemporary lensing surveys z = 0−1. We construct comparable weighted samples from the three simulations, which we then analyse using an alignment model that includes both linear and quadratic alignment contributions. Our data vector includes galaxy–galaxy, galaxy–shape, and shape–shape projected correlations, with the joint covariance matrix estimated analytically. In all of the simulations, we report non-zero IAs at the level of several σ. For a fixed lower mass threshold, we find a relatively strong redshift dependence in all three simulations, with the linear IA amplitude increasing by a factor of ∼2 between redshifts z = 0 and z = 1. We report no significant evidence for non-zero values of the tidal torquing amplitude, A2, in TNG, above statistical uncertainties, although MBII favours a moderately negative A2 ∼ −2. Examining the properties of the TATT model as a function of colour, luminosity and galaxy type (satellite or central), our findings are consistent with the most recent measurements on real data. We also outline a novel method for constraining the TATT model parameters directly from the pixelized tidal field, alongside a proof-of-concept exercise using TNG. This technique is shown to be promising, although comparison with previous results obtained via other methods is non-trivial.

Sign in / Sign up

Export Citation Format

Share Document