An accurate and fair assessment of the efficiency and impact of scientific work is, despite a lot of recent research effort, still an open problem. The measurement of quality and success of individual scientists and research groups can be approached from many different directions, none of which is universal. A reason for this is inherently different behavior of different scientists within the global research community. A complex evaluation of ones publication activities requires a careful consideration of a wide variety of factors. The well-known H-index is one of the most used bibliometric indices. Despite its many imperfections, its simplicity and ease of interpretation make it a popular scientometric method. This short paper uses the ideas behind the H-index to analyze communities of authors who cite publishing scientists. A new author evaluation measure named aH-index is proposed, and intuitive interpretations of its properties and semantics are presented. Preliminary experiments with authors with high H-index active in the area of computer science are presented to demonstrate the properties of the proposed measure.
After the introduction of the , many improvements that addressed its fundamental drawbacks were proposed. In @cite_5 , Zhang proposed a new index that is suitable for evaluating highly cited scientists and comparing groups of scientists with an identical . present in @cite_10 a comprehensive review on the and related indicators. They studied their main advantages, drawbacks, and main applications. In @cite_11 , present a study of 37 different variants of . They show a high correlation between the and most of its variants.
@cite_5 @cite_10 @cite_11
2169089330 2108695571 86604388 1982218215 1978868011 2029099433 2040906208 2160266360 1965655044 2278055525 1502916507 2130953635 2024296872 1973800825 2017562693 2082729696 2910630904 2663075978 2135790056 2100240666
The h-index and some related bibliometric indices have received a lot of attention from the scientific community in the last few years due to some of their good properties (easiness of computation, balance between quantity of publications and their impact and so on). Many different indicators have been developed in order to extend and overcome the drawbacks of the original Hirsch proposal. In this contribution we present a comprehensive review on the h-index and related indicators field. From the initial h-index proposal we study their main advantages, drawbacks and the main applications that we can find in the literature. A description of many of the h-related indices that have been developed along with their main characteristics and some of the works that analyze and compare them are presented. We also review the most up to date standardization studies that allow a fair comparison by means of the h-index among scientists from different research areas and finally, some works that analyze the computation of the h-index and related indices by using different citation databases (ISI Citation Indexes, Google Scholar and Scopus) are introduced. Background The h-index has already been used by major citation databases to evaluate the academic performance of individual scientists. Although effective and simple, the h-index suffers from some drawbacks that limit its use in accurately and fairly comparing the scientific output of different researchers. These drawbacks include information loss and low resolution: the former refers to the fact that in addition to h2 citations for papers in the h-core, excess citations are completely ignored, whereas the latter means that it is common for a group of researchers to have an identical h-index. Methodology Principal Findings To solve these problems, I here propose the e-index, where e2 represents the ignored excess citations, in addition to the h2 citations for h-core papers. Citation information can be completely depicted by using the h-index together with the e-index, which are independent of each other. Some other h-type indices, such as a and R, are h-dependent, have information redundancy with h, and therefore, when used together with h, mask the real differences in excess citations of different researchers. Conclusions Significance Although simple, the e-index is a necessary h-index complement, especially for evaluating highly cited scientists or for precisely comparing the scientific output of a group of scientists having an identical h-index. We show that the standard beam-search algorithm can be used as an efficient decoder for the global linear model of Zhang and Clark (2008) for joint word segmentation and POS-tagging, achieving a significant speed improvement. Such decoding is enabled by: (1) separating full word features from partial word features so that feature templates can be instantiated incrementally, according to whether the current character is separated or appended; (2) deciding the POS-tag of a potential word when its first character is processed. Early-update is used with perceptron training so that the linear model gives a high score to a correct partial candidate as well as a full output. Effective scoring of partial structures allows the decoder to give high accuracy with a small beam-size of 16. In our 10-fold cross-validation experiments with the Chinese Tree-bank, our system performed over 10 times as fast as Zhang and Clark (2008) with little accuracy loss. The accuracy of our system on the standard CTB 5 test was competitive with the best in the literature. Bibliometric measures of individual scientific achievement are of particular interest if they can be used to predict future achievement. Here we report results of an empirical study of the predictive power of the h index compared with other indicators. Our findings indicate that the h index is better than other indicators considered (total citation count, citations per paper, and total paper count) in predicting future scientific achievement. We discuss reasons for the superiority of the h index. An analysis of three major problems in the application of bibliometric research performance indicators is made in three separate sections. In the first section, the influence of field-dependent citation practices is analysed. The results indicate that rankings of publications from different fields, based on citation counts, can be affected seriously by differences between citation characteristics in those fields. If certain assumptions hold, one should expect high (short term) citation levels in Biochemistry, Celbiology and Biophysics. Medium citation levels are to be expected in Experimental and Molecular Physics, Physical and Organic Chemistry, Pharmacology and Plant Physiology, and low citation levels in Mathematics, Taxonomy, Pharmacognosy and Inorganic Solid State Chemistry. In the second section time-dependent factors are studied. It is shown that trend-analyses of output and impact based on bibliometric scores can be disturbed by changes in theSCI-database and in publication and citation practices. One of the disturbing factors is shown to be the inclusion of so called Books into theSCI data-base in 1977. Finally, in the third section a case is presented which illustrates the consequences of operating on incomplete bibliometric data in the evaluation of scientific performance. A completeness percentage of 99 for publication data is proposed as a standard in evaluations of the performance of small university research groups). Citation relationship between scientific publications has been successfully used for scholarly bibliometrics, information retrieval and data mining tasks, and citation-based recommendation algorithms are well documented. While previous studies investigated citation relations from various viewpoints, most of them share the same assumption that, if paper1 cites paper2 (or author1 cites author2), they are connected, regardless of citation importance, sentiment, reason, topic, or motivation. However, this assumption is oversimplified. In this study, we employ an innovative "context-rich heterogeneous network" approach, which paves a new way for citation recommendation task. In the network, we characterize 1) the importance of citation relationships between citing and cited papers, and 2) the topical citation motivation. Unlike earlier studies, the citation information, in this paper, is characterized by citation textual contexts extracted from the full-text citing paper. We also propose algorithm to cope with the situation when large portion of full-text missing information exists in the bibliographic repository. Evaluation results show that, context-rich heterogeneous network can significantly enhance the citation recommendation performance. The paper attempts to provide an alternative method for measuring the importance of scientific papers based on the Google's PageRank. The method is a meaningful extension of the common integer counting of citations and is then experimented for bringing PageRank to the citation analysis in a large citation network. It offers a more integrated picture of the publications' influence in a specific field. We firstly calculate the PageRanks of scientific papers. The distributional characteristics and comparison with the traditionally used number of citations are then analyzed in detail. Furthermore, the PageRank is implemented in the evaluation of research influence for several countries in the field of Biochemistry and Molecular Biology during the time period of 2000-2005. Finally, some advantages of bringing PageRank to the citation analysis are concluded. Spearman's footrule and Kendall's tau are two well established distances between rankings. They, however, fail to take into account concepts crucial to evaluating a result set in information retrieval: element relevance and positional information. That is, changing the rank of a highly-relevant document should result in a higher penalty than changing the rank of an irrelevant document; a similar logic holds for the top versus the bottom of the result ordering. In this work, we extend both of these metrics to those with position and element weights, and show that a variant of the Diaconis-Graham inequality still holds - the generalized two measures remain within a constant factor of each other for all permutations. We continue by extending the element weights into a distance metric between elements. For example, in search evaluation, swapping the order of two nearly duplicate results should result in little penalty, even if these two are highly relevant and appear at the top of the list. We extend the distance measures to this more general case and show that they remain within a constant factor of each other. We conclude by conducting simple experiments on web search data with the proposed measures. Our experiments show that the weighted generalizations are more robust and consistent with each other than their unweighted counter-parts. The popular h-index used to measure scientific output can be described in terms of a pool of evaluated objects (the papers), a quality function on the evaluated objects (the number of citations received by each paper) and a sentencing line crossing the origin, whose intersection with the graph of the quality function yields the index value (in the h-index this is a line with slope 1). Based on this abstraction, we present a new index, the c-index, in which the evaluated objects are the citations received by an author, a group of authors, a journal, etc., the quality function of a citation is the collaboration distance between the authors of the cited and the citing papers, and the sentencing line can take slopes between 0 and ∞. As a result, the new index counts only those citations which are significant enough, where significance is proportional to collaboration distance. Several advantages of the new c-index with respect to previous proposals are discussed. The National Science Foundation (NSF) will be exper- imenting with a new distributed approach to reviewing proposals, whereby a group of principal investigators (PIs) or proposers in a subfield act as reviewers for the proposals submitted by the same set of PIs. To encourage honesty, PIs chances for getting funded are tied to the quality of their reviews (with respect to the reviews provided by the entire group), in addition to the quality of their proposals. Intuitively, this approach can more fairly distribute the review workload, discourage frivolous proposal submission, and encourage high quality reviews. On the other hand, this method has already raised concerns about the integrity of the process and the possibility of strategic manipulation. In this paper, we take a closer look at three specific issues in an attempt to gai n a better understanding of the strengths and limitations of the new process beyond first impressions and anecdotal evidence . We start by considering the benefits and drawbacks of bundling the quality of PIs reviews with the scientific merit of their prop osals. We then consider the issue of collusion and favoritism. Finally, we examine whether the new process puts controversial proposals at a disadvantage. We conclude that some benefits of using revie w quality as an incentive mechanism may outweigh its drawbacks. On the other hand, even a coalition of two PIs can cause signifi cant harm to the process, as the built-in incentives are not strong enough to deter collusion. While we also confirm the common suspicion that the process is skewed toward non-controversial proposals, the more unexpected finding is that among equally controversial proposals, those of lower quality get a leg up through this process. Thus the process not only favors non-controversial proposals, but in some sense, mediocrity. We also discuss possible ways to improve this review process. The nearestor near-neighbor query problems arise in a large variety of database applications, usually in the context of similarity searching. Of late, there has been increasing interest in building search index structures for performing similarity search over high-dimensional data, e.g., image databases, document collections, time-series databases, and genome databases. Unfortunately, all known techniques for solving this problem fall prey to the of dimensionality." That is, the data structures scale poorly with data dimensionality; in fact, if the number of dimensions exceeds 10 to 20, searching in k-d trees and related structures involves the inspection of a large fraction of the database, thereby doing no better than brute-force linear search. It has been suggested that since the selection of features and the choice of a distance metric in typical applications is rather heuristic, determining an approximate nearest neighbor should su ce for most practical purposes. In this paper, we examine a novel scheme for approximate similarity search based on hashing. The basic idea is to hash the points Supported by NAVY N00014-96-1-1221 grant and NSF Grant IIS-9811904. Supported by Stanford Graduate Fellowship and NSF NYI Award CCR-9357849. Supported by ARO MURI Grant DAAH04-96-1-0007, NSF Grant IIS-9811904, and NSF Young Investigator Award CCR9357849, with matching funds from IBM, Mitsubishi, Schlumberger Foundation, Shell Foundation, and Xerox Corporation. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and or special permission from the Endowment. Proceedings of the 25th VLDB Conference, Edinburgh, Scotland, 1999. from the database so as to ensure that the probability of collision is much higher for objects that are close to each other than for those that are far apart. We provide experimental evidence that our method gives signi cant improvement in running time over other methods for searching in highdimensional spaces based on hierarchical tree decomposition. Experimental results also indicate that our scheme scales well even for a relatively large number of dimensions (more than 50). Results of four studies support the notion that anchoring effects are mediated by mechanisms of hypothesis-consistent testing and semantic priming. According to the suggested Selective Accessibility Model, judges use a hypothesis-consistent test strategy to solve a comparative anchoring task. Applying this strategy selectively increases the accessibility of anchor-consistent knowledge which is then used to generate the subsequent absolute judgment. Studies 1 and 2 demonstrate that absolute estimates depend on the hypothesis implied in the comparative task, suggesting that a hypothesis-testing strategy is used to solve this task. Study 3 shows that limiting the amount of knowledge generated for the comparative task retards absolute judgments. This suggests that knowledge rendered easily accessible in the comparative judgment is used for the subsequent absolute judgment. Finally, Study 4 suggests that self-generation of knowledge contributes to the robustness of the effect, thus resolving the seeming inconsistency that anchoring effects are at the same time remarkably robust and mediated by typically fragile semantic priming mechanisms. The objective of this work was to test the relationship between characteristics of an author's network of coauthors to identify which enhance the h-index. We randomly selected a sample of 238 authors from the Web of Science, calculated their h-index as well as the h-index of all co-authors from their h-index articles, and calculated an adjacency matrix where the relation between co-authors is the number of articles they published together. Our model was highly predictive of the variability in the h-index (R 2 = 0.69). Most of the variance was explained by number of co-authors. Other significant variables were those associated with highly productive co-authors. Contrary to our hypothesis, network structure as measured by components was not predictive. This analysis suggests that the highest h-index will be achieved by working with many co-authors, at least some with high h-indexes themselves. Little improvement in h-index is to be gained by structuring a co-author network to maintain separate research communities. Abstract The article introduces a new Hirsch-type index for a scientist. This so-called pure h-index, denoted by hP, takes the actual number of coauthors, and the scientist’s relative position in the byline into account. The transformation from h to hP can also be applied to the R-index, leading to the pure R-index, denoted as RP. This index takes the number of collaborators, possibly the rank in the byline and the actual number of citations into account. In the past several years studies have started to appear comparing the accuracies of various science mapping approaches. These studies primarily compare the cluster solutions resulting from different similarity approaches, and give varying results. In this study we compare the accuracies of cluster solutions of a large corpus of 2,153,769 recent articles from the biomedical literature (2004–2008) using four similarity approaches: co-citation analysis, bibliographic coupling, direct citation, and a bibliographic coupling-based citation-text hybrid approach. Each of the four approaches can be considered a way to represent the research front in biomedicine, and each is able to successfully cluster over 92p of the corpus. Accuracies are compared using two metrics—within-cluster textual coherence as defined by the Jensen-Shannon divergence, and a concentration measure based on the grant-to-article linkages indexed in MEDLINE. Of the three pure citation-based approaches, bibliographic coupling slightly outperforms co-citation analysis using both accuracy measures; direct citation is the least accurate mapping approach by far. The hybrid approach improves upon the bibliographic coupling results in all respects. We consider the results of this study to be robust given the very large size of the corpus, and the specificity of the accuracy measures used. © 2010 Wiley Periodicals, Inc. This paper reports on a novel technique for literature indexing and searching in a mechanized library system. The notion of relevance is taken as the key concept in the theory of information retrieval and a comparative concept of relevance is explicated in terms of the theory of probability. The resulting technique called “Probabilistic Indexing,” allows a computing machine, given a request for information, to make a statistical inference and derive a number (called the “relevance number”) for each document, which is a measure of the probability that the document will satisfy the given request. The result of a search is an ordered list of those documents which satisfy the request ranked according to their probable relevance. The paper goes on to show that whereas in a conventional library system the cross-referencing (“see” and “see also”) is based solely on the “semantical closeness” between index terms, statistical measures of closeness between index terms can be defined and computed. Thus, given an arbitrary request consisting of one (or many) index term(s), a machine can elaborate on it to increase the probability of selecting relevant documents that would not otherwise have been selected. Finally, the paper suggests an interpretation of the whole library problem as one where the request is considered as a clue on the basis of which the library system makes a concatenated statistical inference in order to provide as an output an ordered list of those documents which most probably satisfy the information needs of the user. Abstract This paper presents a robust and comprehensive graph-based rank aggregation approach, used to combine results of isolated ranker models in retrieval tasks. The method follows an unsupervised scheme, which is independent of how the isolated ranks are formulated. Our approach is able to combine arbitrary models, defined in terms of different ranking criteria, such as those based on textual, image or hybrid content representations. We reformulate the ad-hoc retrieval problem as a document retrieval based on fusion graphs , which we propose as a new unified representation model capable of merging multiple ranks and expressing inter-relationships of retrieval results automatically. By doing so, we claim that the retrieval system can benefit from learning the manifold structure of datasets, thus leading to more effective results. Another contribution is that our graph-based aggregation formulation, unlike existing approaches, allows for encapsulating contextual information encoded from multiple ranks, which can be directly used for ranking, without further computations and post-processing steps over the graphs. Based on the graphs, a novel similarity retrieval score is formulated using an efficient computation of minimum common subgraphs. Finally, another benefit over existing approaches is the absence of hyperparameters. A comprehensive experimental evaluation was conducted considering diverse well-known public datasets, composed of textual, image, and multimodal documents. Performed experiments demonstrate that our method reaches top performance, yielding better effectiveness scores than state-of-the-art baseline methods and promoting large gains over the rankers being fused, thus demonstrating the successful capability of the proposal in representing queries based on a unified graph-based model of rank fusions. In this paper, we present the results of a user study on exploratory search activities in a social science digital library. We conducted a user study with 32 participants with a social sciences background—16 postdoctoral researchers and 16 students—who were asked to solve a task on searching related work to a given topic. The exploratory search task was performed in a 10-min time slot. The use of certain search activities is measured and compared to gaze data recorded with an eye tracking device. We use a novel tree graph representation to visualise the users’ search patterns and introduce a way to combine multiple search session trees. The tree graph representation is capable of creating one single tree for multiple users and identifying common search patterns. In addition, the information behaviour of students and postdoctoral researchers is being compared. The results show that search activities on the stratagem level are frequently utilised by both user groups. The most heavily used search activities were keyword search, followed by browsing through references and citations, and author searching. The eye tracking results showed an intense examination of documents metadata, especially on the level of citations and references. When comparing the group of students and postdoctoral researchers, we found significant differences regarding gaze data on the area of the journal name of the seed document. In general, we found a tendency of the postdoctoral researchers to examine the metadata records more intensively with regard to dwell time and the number of fixations. By creating combined session trees and deriving subtrees from those, we were able to identify common patterns like economic (explorative) and exhaustive (navigational) behaviour. Our results show that participants utilised multiple search strategies starting from the seed document, which means that they examined different paths to find related publications. Researchers have access to large online archives of scientific articles. As a consequence, finding relevant papers has become more difficult. Newly formed online communities of researchers sharing citations provides a new way to solve this problem. In this paper, we develop an algorithm to recommend scientific articles to users of an online community. Our approach combines the merits of traditional collaborative filtering and probabilistic topic modeling. It provides an interpretable latent structure for users and items, and can form recommendations about both existing and newly published articles. We study a large subset of data from CiteULike, a bibliography sharing service, and show that our algorithm provides a more effective recommender system than traditional collaborative filtering. In this paper we present some new methods of ranking information retrieval systems without relevance judgement. The common ground of these methods is using a measure we called reference count. An extensive experimentation was conducted to evaluate the effectiveness of the proposed methods using various different standards Information Retrieval evaluation measures for the ranking, like average precision, R-precision, and precision and different document levels. We also compared the effectiveness of the proposed methods with the method proposed by The experimental results showed that the proposed methods are effective, and in many cases are more effective than Soboroff at al.'s method.