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Generalized comparison of graph-based ranking algorithms for publications and authors

JOURNAL OF SYSTEMS AND SOFTWARE 2006年第12期79卷 1679-1700页

作者： Sidiropoulos, Antonis Manolopoulos, Yannis Aristotle Univ Thessaloniki Data Engn Lab Dept Informat Thessaloniki 54124 Greece

Citation analysis helps in evaluating the impact of scientific collections (journals and conferences), publications and scholar authors. In this paper we examine known algorithms that are currently used for Link Analysis ranking, and present their weaknesses over specific examples. We also introduce new alternative methods specifically designed for citation graphs. We use the SCEAS system as a base platform to introduce these new methods and perform a generalized comparison of all methods. We also introduce an aggregate function for the generation of author ranking based on publication ranking. Finally, we try to evaluate the rank results based on the prizes of 'VLDB 10 Year Award', 'SIGMOD Test of Time Award' and 'SIGMOD E. F. Codd Innovations A ward'. (C) 2006 Elsevier Inc. All rights reserved.

关键词： SCEAS system ranking algorithms citation analysis citation graphs

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A FORTRAN-IV PROGRAM FOR COMPARING ranking algorithms IN QUANTITATIVE BIOSTRATIGRAPHY

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COMPUTERS & GEOSCIENCES 1984年第1期10卷 3-29页

作者： HARPER, CW School of Geology and Geophysics University of Oklahoma 830 Van Vleet Oval Rm. 109 Norman OK 73019 U.S.A.

A program for evaluating the performance of competing ranking algorithms in stratigraphic paleontology is presented. The program (1) generates a hypothetical, and thus known, succession of taxa in time and (2) simulates their succession in strata at several local sample sites. If desired, (1) and (2) may be repeated for several (=50 or 100 for example) iterations and the local site data for each sent to two user routines for inferred rankings (inferred succession of events in time). First data for first and last occurrences (fads and lads) taken together, then data for for lads-only, then data for fads-only is sent. For each submission of data to a user routine, Kendall rank correlation coefficients and Spearman coefficients are computed comparing the inferred rankings generated by the user routine with the known succession of events in time. The performance of two competing ranking algorithms may be compared by (1) obtaining for each submitted dataset the differences between corresponding Kendall (and/or Spearman) coefficients computed for the two algorithms, and (2) testing the observed differences for statistical significance. A simple two-sided t -test may be used to test whether the observed mean difference between two corresponding coefficients differs significantly from zero; if c t -tests are performed, the level of significance of each should be set to alpha/ c to obtain a maximum experimentwise error rate of less than alpha. The program is used to compare three ranking algorithms provided by Agterberg and Nel (1982a, b) as well as to determine whether the algorithms work as well for datasets combining lads and fads vs datasets for lads-only or fads-only. Agterberg and Nel's Presorting algorithm performed better than their ranking or Scaling algorithm. All three performed slightly but significantly better on data for lads-only or fads-only as opposed to combined data.

关键词： Biostratigraphy ranking algorithms Simulation Statistics Paleontology

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Novel learning framework (knockoff technique) to evaluate metric ranking algorithms to describe human response to injury

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TRAFFIC INJURY PREVENTION 2018年第Sup2期19卷 S121-S126页

作者： Banerjee, Anjishnu DeVogel, Nicholas Pintar, Frank A. Yoganandan, Narayan Med Coll Wisconsin Div Biostat Milwaukee WI 53226 USA Med Coll Wisconsin Dept Neurosurg 9200 West Wisconsin Ave Milwaukee WI 53226 USA VA Med Ctr Milwaukee WI USA Med Coll Wisconsin Ctr NeuroTrauma Res Milwaukee WI 53226 USA

Objective: The objective of this study is to present a novel framework, termed the knockoff technique, to evaluate different metric ranking algorithms to better describe human response to injury. Methods: Many biomechanical metrics are routinely obtained from impact tests using postmortem human surrogates (PMHS) to develop injury risk curves (IRCs). The IRCs form the basis to evaluate human safety in crashworthiness environments. The biomechanical metrics should be chosen based on some measure of their predictive ability. Commonly used algorithms for the choice of ranking the metrics include (a) areas under the receiver operating characteristic curve (AUROC), time-varying AUROC, and other adaptations, and (b) some variants of predictive squared error loss. This article develops a rigorous framework to evaluate the metric selection/ranking algorithms. Actual experimental data are used due to the shortcoming of using simulated data. The knockoff data are meshed into existing experimental data using advanced statistical algorithms. Error rate measures such as false discovery rates (FDRs) and bias are calculated using the knockoff technique. Experimental data are used from previously published whole-body PMHS side impact sled tests. The experiments were conducted at different velocities, padding and rigid load wall conditions, and offsets and with different supplemental restraint systems. The PMHS specimens were subjected to a single lateral impact loading resulting in injury and noninjury outcomes. Results: A total of 25 metrics were used from 42 tests. The AUROC-type algorithms tended to have higher FDRs compared to the squared error loss-type functions (45.3% for the best AUROC-type algorithms versus 31.4% for the best Brier score algorithm). Standard errors for the Brier score algorithm also tended to be lower, indicative of more stable metric choices and robust rankings. The wide variations observed in the performance of the algorithms demonstrated the need for dat

关键词： Injury risk curves ranking algorithms knockoff technique false discovery rate bias measurement data augmentation

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

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SCIENTOMETRICS 2022年第7期127卷 4045-4074页

作者： Zhang, Yu Wang, Min Saberi, Morteza Chang, Elizabeth UNSW Canberra Sch Business Northcott Dr Canberra ACT 2612 Australia UNSW Canberra Sch Engn & Informat Technol Northcott Dr Canberra ACT 2612 Australia Univ Technol Sydney Fac Engn & Informat Technol 15 Broadway Sydney NSW 2007 Australia

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.

关键词： Academic paper ranking ranking algorithms Bibliometric analysis Citation analysis

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Comparison of ranking algorithms with Dataspace 2

Comparison of Ranking Algorithms with Dataspace

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International Conference on Advances in Computer Engineering and Applications (ICACEA)

作者： Lal, Niranjan Qamar, Samimul Suresh Gyan Vihar Univ Comp Sci & Engn Jaipur Rajasthan India King Khalid Univ Comp Network Engn Dept Comp Sci & Engn Abha Saudi Arabia

ISBN: (纸本)9781467369114

With increased in digitization the amount of homogeneous, unstructured, semi-structured, structured or heterogeneous data being created and stored is exploding is collectively called "Dataspace". Data being generated from various heterogeneous sources like, digital images, audio, video, online transactions, online social media, data from sensor nodes, click streams for different domains including, retails, medical, healthcare, energy, and day to day life utilities. In business, industries, institutions and organizations, individuals contribute the data volume like technical reports, seminar reports, research papers, dissertations, thesis etc. For instance, 30 billion web pages are accessed or the World Wide Web. With terrific number of pages of that exist today;search engines assume a significant role in the current internet of thing (IOT). So with billions of web pages accessible on the web, a user query entered in the search engine may returns thousands of web pages, and thus it becomes extremely important to rank these results in such a way that the most "related" or "important" or "authorized" pages are displayed first. This job of prioritizing the results is performed by ranking algorithms, and various search engines use different schemes for ranking the results. ranking of data can also do in heterogeneous data to retrieve information from the Dataspace. The aim of this paper is to describe Dataspace and present a survey on ranking algorithms, and their comparison, Comparison is done on the basis of some parameters such as main technique use, methodology, and input parameter, and relevancy, quality of results, importance and limitations, search engines and time complexity of algorithms. In this we also explained how ranking can be used in Dataspace with challenges to information retrieval from heterogeneous data or from Dataspace.

关键词： Dataspace ranking database ranking ranking algorithms IOT, search engines algorithms, information retrieval

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ranking algorithms for Lists of Partitions

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SIAM Journal on Computing 1976年第4期5卷 602-617页

作者： S. G. Williamson

Given an algorithm for producing a list of objects, a “ranking” algorithm or “sequential numbering scheme“ is a rule $\rho $ which, given an object x, computes its position $m = \rho (x)$ in the list. The associated algorithm for computing $\rho ^{ - 1} $, produces the object x given its position m. In this paper, we consider the objects to be various classes of partitions of a set S. We consider ranking algorithms for lists of all ordered partitions, ordered partitions corresponding to a given multinomial index, all unordered partitions, unordered partitions with a bounded number of blocks, unordered partitions with a fixed number of blocks, unordered partitions with blocks of equal size, unordered partitions corresponding to a fixed ordered partition, and unordered partitions of specified block type. These ranking algorithms are frequently useful for organizing computations associated with the above lists. The computation of $\rho ^{ - 1} $ at a randomly selected m provides a method for the random selection of elements from any of these lists.

关键词： ordered set partitions unordered set partitions ranking algorithms lexicographic order

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ranking algorithms: The Symmetries and Colorations of the

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SIAM Journal on Computing 1976年第2期5卷 297-304页

作者： Jay P. Fillmore S. G. Williamson

This paper discusses determination of the ranks, in lexicographic lists, of the symmetries and proper symmetries of the n-cube and of its colorations by r colors, up to these symmetries.

关键词： combinatorial algorithms ranking algorithms serial numbering schemes -cube

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Page ranking algorithms used in Web Mining

Page Ranking Algorithms used in Web Mining

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International Conference on Information Communication and Embedded Systems (ICICES)

作者： Sangeetha, M. Joseph, K. Suresh Pondicherry Univ Sch Engn & Technol Dept Comp Sci Pondicherry India

ISBN: (纸本)9781479938346

The world wide web is expanding, everyday huge amount of data is added to the web. Finding relevant information in the web is becoming a difficult task. Web Mining is the process of analysing and mining the web to find useful information. By web mining we extract information that are implicitly present in the web. Web Mining is classified into Web Content Mining (WCM), Web Structure Mining (WSM), Web Usage Mining (WUM) based on the type of data mined. Web Structure Mining analyses the structure of the web considering it as a graph. WSM can be used to rank pages present in the web, to improve the efficiency of search engines. This paper discusses about Web Mining, its types, and various ranking algorithms used in Web structure Mining.

关键词： Web Mining ranking algorithms Web Structure Mining

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A Review on Different ranking algorithms

A Review on Different Ranking Algorithms

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2018 IEEE International Conference on Advances in Computing, Communication Control and Networking, ICACCCN 2018

作者： Irfan, Shadab Ghosh, Subhajit Galgotias University Greater Noida Uttar Pradesh India

ISBN: (纸本)9781538641194

In today scenario with the growing population the need of the user is mounting at a fast speed. As the demand for fulfilling the need is increasing, it results in massive growth of data. To handle such vast amount of data is really a cumbersome task, so different mining techniques are used to handle and manage this amount of data. In order to cater the needs of user and to get the desired information from such pool of data the web engines uses different techniques which help in managing the information properly and to retrieve the desired information in less amount of time. Various ranking algorithms are used which help to retrieve the highly desirable page according to the query entered by the user. This paper provides a brief glimpse of different ranking algorithms. © 2018 IEEE.

关键词： Modern Algorithm ranking algorithms Web Mining

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PerfoRank: cluster-based performance ranking for improved performance evaluation and estimation in professional cycling

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MACHINE LEARNING 2025年第1期114卷 1-30页

作者： Janssens, Bram Bogaert, Matthias Univ Ghent Dept Mkt Innovat & Org Tweekerkenstr 2 B-9000 Ghent Belgium FlandersMake UGentcorelab CVAMO B-9000 Ghent Belgium Res Fdn Flanders Rue Louvain 38 B-1000 Brussels Belgium

Current cycling analytics solutions do not account for the race course profile or the level of the competition. Therefore, this paper develops a unique two-stage clustering-based ranking approach for rider evaluation. Initially, races are segmented into coherent clusters based upon elevation and road surface type. Subsequently, underlying skill levels are determined per cluster through the observed race results using the TrueSkill algorithm which allows to model multi-entrant competitions. The results indicate that our approach uncovers clusters which match the commonly known specializations in road cycling. The ranking methodology generates skill ratings which enable the identification of specialization and can be used in downstream tasks. Our results show that the proposed rankings drastically improve race outcome estimation when adding these rankings as features to the current prediction models.

关键词： Sports analytics Cycling analytics Performance evaluation ranking algorithms Statistically enhanced learning

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