Social Networking Techniques for Ranking

1. Social Networking Techniques for Ranking Scientific Publications (i.e. Conferences & journals) and Research Scholars

2. Introduction Ranking Scientific Publications By using: • H-Index and • Impact Factors

3. Examples – Expertise search • When starting a work in a new research topic; • Or brainstorming for novel ideas. ? • Who are experts in this field? • What are the top conferences in the field? • What are the best papers? • What are the top research labs? Researcher

4. H-Index

5. H-Index Use of H-Index: • Serves as an alternative to more traditional journal Impact factor metrics in the evaluation of the impact of the work of particular researcher. • Little systematic investigation has been made on how academic recognition correlates with h-index over different institutions, nations and fields of study. For example, Hirch (inventor of H-index) suggested that:- • for physicists, a value for h of about 12 might be typical for advancement to tenure (associate professor) at major research universities. • A value of about 18 could mean a full professorship, • 15–20 could mean a fellowship in the American Physical Society, and • 45 or higher could mean membership in the United States National Academy of Sciences.

6. H-Index Calculation: The h-index can be manually determined using citation databases or using automatic tools. The dataset can be obtained from (1) Google Scholar entries, (2) DBLP database, or any other citation networks. Advantages: The h-index is intended to measure simultaneously the quality and quantity of scientific output. Demerits: There are a number of situations in which h may provide misleading information about a scientist's output: (However, most of these are not exclusive to the h-index.) • The h-index does not account for the number of authors of a paper. • The h-index does not account for the typical number of citations in different fields. Different fields, or journals, traditionally use different numbers of citations.

7. H-Index H-Index, Demerits: • The h-index discards the information contained in author placement in the authors' list, which in some scientific fields (but not in high energy physics, where Hirsch works) is significant. • The h-index is bounded by the total number of publications. • The h-index does not consider the context of citations. • The h-index gives books the same count as articles making it difficult to compare scholars in fields that are more book-oriented such as the humanities. • The h-index does not account for confounding factors such as "gratuitous authorship", the so-called Matthew effect, and the favorable citation bias associated with review articles. • The h-index has been found to have slightly less predictive accuracy and precision than the simpler measure of mean citations per paper. • The h-index is a natural number which reduces its discriminatory power. The h-index can be manipulated through self-citations and if based on Google Scholar output, then even computer-generated documents can be used for that purpose, e.g. using SCIgen.

8. H-Index Alternatives and Modifications • An individual h-index normalized by the average number of co-authors in the h-core has been introduced by Batista et al. • The m-index is defined as h/n, where n is the number of years since the first published paper of the scientist; also called m-quotient. • A generalization of the h-index and some other indices that gives additional information about the shape of the author's citation function (heavy-tailed, flat/peaked, etc.) was proposed by Gągolewski and Grzegorzewski. • Successive Hirsch-type-index introduced independently by Kosmulski and Prathap.

9. H-Index Alternatives and Modifications • K. Dixit and colleagues argue that "For an individual researcher, a measure such as Erdős number captures the structural properties of network whereas the h-index captures the citation impact of the publications. • The c-index accounts not only for the citations but for the quality of the citations in terms of the collaboration distance between citing and cited authors. A scientist has c-index n if n of [his/her] N citations are from authors which are at collaboration distance at least n, and the other (N − n) citations are from authors which are at collaboration distance at most n. • Bornmann, Mutz, and Daniel recently proposed three additional metrics, h2lower, h2center, and h2upper, to give a more accurate representation of the distribution shape.

10. H-Index

11. Impact Factor

12. Impact Factor Impact Factor: Importance • The impact factor is highly discipline-dependent. • The impact factor could not be reproduced in an independent audit. • The impact factor refers to the average number of citations per paper, but this is not a normal distribution. It is rather a Bradford distribution, as predicted by theory. • Journal ranking lists constructed based on the impact factor moderately correlate with journal ranking lists based on the results of an expert survey.

13. Impact Factor Example Estimated Impact Factors of Computer Science Conferences, By CiteSeer: (http://citeseer.ist.psu.edu/stats/venues?y=2007) Generated from documents in the CiteSeerx database as of March 20, 2008. This list is automatically generated and may contain errors. Impact is estimated based on Garfield's traditional impact factor. Venue details obtained from DBLP by Michael Ley. Only venues contained in DBLP are included. • POPL 0.45 • OSDI 0.43 • PLDI 0.4 • ACM Conference on Computer and Communications Security 0.39 • S&P 0.37 • NSDI 0.37 • CSFW 0.33 • ASPLOS 0.32 • SIGCOMM 0.31 • RAID 0.31

14. Impact Factor Example

15. References

16. References