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
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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. 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. A new form of document coupling called co-citation is defined as the frequency with which two documents are cited together. The co-citation frequency of two scientific papers can be determined by comparing lists of citing documents in the Science Citation Index and counting identical entries. Networks of co-cited papers can be generated for specific scientific specialties, and an example is drawn from the literature of particle physics. Co-citation patterns are found to differ significantly from bibliographic coupling patterns, but to agree generally with patterns of direct citation. Clusters of co-cited papers provide a new way to study the specialty structure of science. They may provide a new approach to indexing and to the creation of SDI profiles. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results.