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Dalibor Fiala 《Journal of Informetrics》2012,6(3):370-388
In the past, recursive algorithms, such as PageRank originally conceived for the Web, have been successfully used to rank nodes in the citation networks of papers, authors, or journals. They have proved to determine prestige and not popularity, unlike citation counts. However, bibliographic networks, in contrast to the Web, have some specific features that enable the assigning of different weights to citations, thus adding more information to the process of finding prominence. For example, a citation between two authors may be weighed according to whether and when those two authors collaborated with each other, which is information that can be found in the co-authorship network. In this study, we define a couple of PageRank modifications that weigh citations between authors differently based on the information from the co-authorship graph. In addition, we put emphasis on the time of publications and citations. We test our algorithms on the Web of Science data of computer science journal articles and determine the most prominent computer scientists in the 10-year period of 1996–2005. Besides a correlation analysis, we also compare our rankings to the lists of ACM A. M. Turing Award and ACM SIGMOD E. F. Codd Innovations Award winners and find the new time-aware methods to outperform standard PageRank and its time-unaware weighted variants. 相似文献
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This study examined the effectiveness of a paired reading intervention using curriculum‐based measurement (CBM) probes to document reading progress for one fourth‐ and two third‐grade students. These students were considered below‐average readers. Parents and children used the paired reading method in their homes for 10 minutes, four times a week. Children and parents were also provided with reading material at their instructional level. Results showed that the children's reading accuracy and rates increased from baseline to follow‐up on CBM measures and on pre/posttesting. In addition, students and parents gave favorable treatment acceptability ratings for paired reading. Implications for future research are outlined. © 2003 Wiley Periodicals, Inc. Psychol Schs 40: 613–626, 2003. 相似文献
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The paper proposes an innovative procedure for benchmarking the digital divide. The study demonstrates the potential of an outranking approach as an alternative to the commonly used ranking models based on Composite Indices (CIs). To fulfill the objectives of the benchmarking process, an ELECTRE-based (ELimination Et Choix Traduisant la REalité; Elimination and Choice Corresponding to Reality) multi-level outranking (ELECTRE MLO) method is developed. The proposed approach improves on previous methods of benchmarking the digital divide in two ways: by classifying countries into hierarchical levels of performance and by identifying corresponding benchmarks for less successful ones. The method is applied to 29 EBRD (European Bank for Reconstruction and Development) countries of operation. The results are visualized in the form of a relation tree, thus providing clear insights for policy-makers regarding how countries stand relative to each other. 相似文献
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This article describes the results of our analysis of the data from the CiteSeer digital library. First, we examined the data from the point of view of source top-level Internet domains from which the data were collected. Second, we measured country shares in publications indexed by CiteSeer and compared them to those based on mainstream bibliographic data from the Web of Science and Scopus. And third, we concentrated on analyzing publications and their citations aggregated by countries. This way, we generated rankings of the most influential countries in computer science using several non-recursive as well as recursive methods such as citation counts or PageRank. We conclude that even if East Asian countries are underrepresented in CiteSeer, its data may well be used along with other conventional bibliographic databases for comparing the computer science research productivity and performance of countries. 相似文献
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Convexity in a network (graph) has been recently defined as a property of each of its subgraphs to include all shortest paths between the nodes of that subgraph. It can be measured on the scale [0, 1] with 1 being assigned to fully convex networks. The largest convex component of a graph that emerges after the removal of the least number of edges is called a convex skeleton. It is basically a tree of cliques, which has been shown to have many interesting features. In this article the notions of convexity and convex skeletons in the context of scientific collaboration networks are discussed. More specifically, we analyze the co-authorship networks of Slovenian researchers in computer science, physics, sociology, mathematics, and economics and extract convex skeletons from them. We then compare these convex skeletons with the residual graphs (remainders) in terms of collaboration frequency distributions by various parameters such as the publication year and type, co-authors’ birth year, status, gender, discipline, etc. We also show the top-ranked scientists by four basic centrality measures as calculated on the original networks and their skeletons and conclude that convex skeletons may help detect influential scholars that are hardly identifiable in the original collaboration network. As their inherent feature, convex skeletons retain the properties of collaboration networks. These include high-level structural properties but also the fact that the same authors are highlighted by centrality measures. Moreover, the most important ties and thus the most important collaborations are retained in the skeletons. 相似文献
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