text: 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.
  @cite_1: 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.
  @cite_4: 
  @cite_7: 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.
summary: The complicated relationship between the scientist and his or her co-authors is one of the problems of the . Hirsch proposes in @cite_4 a new version of the that takes into account the effect of multiple co-authors and solves a well-known problem with the so-called Hirsch core. A similar problem is solved and a new variant of the is introduced by in @cite_1 . In @cite_7 , apply social network analysis on ego co-authorship network. They show that the highest can be achieved by working with many co-authors.