Generalized h-index for Revealing Latent Facts in Social Networks of Citations

1. Generalized h-index for Revealing Latent Facts in Social Networks of Citations A. Sidiropoulos, D. Katsaros, Y. Manolopoulos @ Department of Informatics Aristotle University, Thessaloniki, Greece http://skyblue.csd.auth.gr/~{asidirop,dimitris,manolopo} Presentation by:Panagiotis Symeonidis @ Department of Informatics Aristotle University, Thessaloniki, Greece ACM LinkKDD: 20/08/2006

2. Methods for Ranking Scientists • Evaluation of scientists by “experts” • e.g., surveys • Citation Analysis • Task: Compute a score for the “objects” • Hybrid method of previous two. ACM Workshop on Link Analysis (LinkKDD): Dynamics and Static of Large Networks

3. Drawbacks of various scientists ranking methods • Not measure the importance of papers • Affected by “big hits” • Not measure productivity • Need to set administrative parameters ACM Workshop on Link Analysis (LinkKDD): Dynamics and Static of Large Networks

4. H-index • Proposed by J.E. Hircsh in Oct. 2005 • Definition:A researcher has h-index h if • h of his Np articles have received at least h citations each • the rest Np-h articles have received no more than h citations each ACM Workshop on Link Analysis (LinkKDD): Dynamics and Static of Large Networks

5. H-index • Calculates the broadness of a researcher • Productivity • Impact • Not affected by “big hits” • Not affected by “noise” ACM Workshop on Link Analysis (LinkKDD): Dynamics and Static of Large Networks

6. Index a • Nc,tot≥h2 • Definition:A researcher has index a if Nc,tot=ah2 • Second metric-index ACM Workshop on Link Analysis (LinkKDD): Dynamics and Static of Large Networks

7. H-index drawbacks • It is a growing function over time • Does not show scientist’s inactivity or retirement • Scientists with short scientific life are out of competition ACM Workshop on Link Analysis (LinkKDD): Dynamics and Static of Large Networks

8. Contemporary H-index • Definition:A researcher has contemporary h-index hc if • hc of his Np articles have Sc(i)≥hc • the rest Np-hc articles have Sc(i)≤hc • Sc(i)= * (Y(now) - Y(i) + 1)- |C(i)| • In our experiments: =4 and =1 • An old article gradually loses its “value” • Show how “active” a researcher is. ACM Workshop on Link Analysis (LinkKDD): Dynamics and Static of Large Networks

9. Trend H-index • Definition:A researcher has trend h-index ht if • htof his Np articles have St(i)≥h • the rest Np-ht articles have St(i)≤h • In our experiments: =4 and =1 • An old citation gradually loses its “value” • Shows how “trendy” the work of a researcher is. ACM Workshop on Link Analysis (LinkKDD): Dynamics and Static of Large Networks

10. Normalized H-index • Definition:A researcher has normalized h-index hn=h/Np ACM Workshop on Link Analysis (LinkKDD): Dynamics and Static of Large Networks

11. H-index generalizations • Contemporary • Trend • Normalized • Scientists, journals, conferences or any other kind of semantic grouping of articles. ACM Workshop on Link Analysis (LinkKDD): Dynamics and Static of Large Networks

12. H-index for conferences and journals • Yearly h-index • Definition:A conference or journal has yearly h-index hy for the year y if • hy of its articles Np,y published during the year y have received ≥hy citations each • and the rest (Np,y-hy) articles received ≤hycitations each. ACM Workshop on Link Analysis (LinkKDD): Dynamics and Static of Large Networks

13. H-index for conferences and journals • Normalized Yearly h-index • Definition:A conference or journal has Normalized yearly h-indexhyn= hy/Np,y ACM Workshop on Link Analysis (LinkKDD): Dynamics and Static of Large Networks

14. Experiments • DBLP collection (http://dblp.uni-trier.de/) • Data timestamp: March 2006 • DBLP includes data for authors, journals and conferences • Focuses in the DB area • “Names Problem” is solved ACM Workshop on Link Analysis (LinkKDD): Dynamics and Static of Large Networks

15. Experiments – h-index ACM Workshop on Link Analysis (LinkKDD): Dynamics and Static of Large Networks

16. Experiments – Contemporary h-index ACM Workshop on Link Analysis (LinkKDD): Dynamics and Static of Large Networks

17. Experiments - Trend h-index ACM Workshop on Link Analysis (LinkKDD): Dynamics and Static of Large Networks

18. Experiments – h-index for scientists ACM Workshop on Link Analysis (LinkKDD): Dynamics and Static of Large Networks

19. Experiments – h-index for scientists ACM Workshop on Link Analysis (LinkKDD): Dynamics and Static of Large Networks

20. Experiments – H-index for conferences ACM Workshop on Link Analysis (LinkKDD): Dynamics and Static of Large Networks

21. Experiments – H-index for conferences ACM Workshop on Link Analysis (LinkKDD): Dynamics and Static of Large Networks

22. Experiments – H-index for conferences ACM Workshop on Link Analysis (LinkKDD): Dynamics and Static of Large Networks

23. Experiments – H-index for journals ACM Workshop on Link Analysis (LinkKDD): Dynamics and Static of Large Networks

24. Experiments – H-index for journals ACM Workshop on Link Analysis (LinkKDD): Dynamics and Static of Large Networks

25. Experiments – H-index for journals ACM Workshop on Link Analysis (LinkKDD): Dynamics and Static of Large Networks

26. Conclusions • Evaluation of scientists based on citation analysis • Evaluation of publication forums based on citation analysis • H-index shortcomings: • Active – inactive scientists • Significant works in the past – not any more significant • H-index generalizations along the time dimension ACM Workshop on Link Analysis (LinkKDD): Dynamics and Static of Large Networks

27. Generalized h-index for Revealing Latent Facts in Social Networks of Citations Antonis Sidiropoulos Dimitrios Katsaros Yannis Manolopoulos Thank you for your attention! The authors would greatly appreciate your comments! Presenter: Panagiotis Symeonidis ACM Workshop on Link Analysis (LinkKDD): Dynamics and Static of Large Networks