Delineating the citation impact of scientific discoveries

Proceedings of the ACM International Conference on Digital Libraries

Volumn , Issue , 2007, Pages 19-28

  Chen, Chaomei ^a   Zhang, Jian ^a   Zhu, Weizhong ^a   Vogeley, Michael ^a  

^a DREXEL UNIVERSITY   (United States)

Author keywords

H index; Scientific discoveries; Sloan digital sky survey; Social networks; Visualization

Indexed keywords

KNOWLEDGE DIFFUSION; OPTIMAL THRESHOLDS; SCIENTIFIC DISCOVERIES; SOCIAL NETWORKS;

COMPUTER VISION; DATA MINING; DECISION TREES; INNOVATION; STATISTICAL METHODS; THRESHOLD LOGIC;

KNOWLEDGE BASED SYSTEMS;

EID: 36349029240     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1255175.1255179     Document Type: Conference Paper

References (42)

1. Abt, H.A. Astronomical publication in the near future. Publications of the Astronomical Society of the Pacific, 112, (2000), 1417-1420.
2. Abt, H.A. Do important papers produce high citation counts. Scientometrics, 48, (2000), 65-70.
3. Abt, H.A. Some trends in American astronomical publications. Publications of the Astronomical Society of the Pacific, 553, (1981), 269-272.
4. Abt, H.A. Why some papers have long citation lifetimes. Nature, 395, (1998), 756-757.
5. Ackermann, E. Indicators of failed information epidemics in the scientific journal literature: A publication analysis of Polywater and Cold Nuclear Fusion. Scientometrics, 66, 3 (2006), 15.
6. Barabási, A.L., Jeong, H., Néda, Z, Ravasz, E., Schubert, A. and Vicsek, T. Evolution of the social network of scientific collaborations. Physica A, 311, (2002), 590-614.
7. Bush, V. As we may think. The Atlantic Monthly, 176, 1 (1945), 101-108.
8. Chen, C. CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57, 3 (2006), 359-377.
9. Chen, C. Mapping Scientific Frontiers: The Quest for Knowledge Visualization. Springer, London, 2003.
10. Chen, C. Searching for intellectual turning points:
11. Progressive Knowledge Domain Visualization. Proc. Natl. Acad. Sci. USA, 101, Suppl. (2004), 5303-5310.
12. Chen, C., Ibekwe-SanJuan, F., SanJuan, E. and Weaver, C., Visual Analysis of Conflicting Opinions, in Proceedings of the IEEE Symposium on Visual Analytics Science and Technology (VAST), (Baltimore, MA, 2006), 2006, 59-66.
13. Daim, T.U., Rueda, G., Martin, H. and Gerdsri, P. Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting and Social Change, 73, (2006), 31.
14. Davoust, E. and Schmadel, L.D. A study of the publishing activity of astronomers since 1969. Scientometrics, 22, (1991), 9-39.
15. Dunbar, K. Concept discovery in a scientific domain. Cognitive Science, 17, (1993), 397-434.
16. Dunning, T. Accurate methods for the statistics of surprise and coincidence. Computational Linguistics, 19, 1 (1993), 61-74.
17. Fernandez, J.A. The transition from an individual science to a collective one: The case of astronomer. Scientometrics, 42, (1998), 61-74.
18. Freund, Y. and Mason, L. The alternating decision tree learning algorithm Proceeding of the Sixteenth International Conference on Machine Learning, Bled, Slovenia, 1999.
19. Hargens, L.L. Using the Literature: Reference Networks, Reference Contexts, and the Social Structure of Scholarship. American Sociological Review, 65, 6 (2000), 846-865.
20. Hirsch, J.E. An index to quantify an individual's scientific research output. PNAS, 102, (2005), 16569-16572.
21. Kleinberg, J., Bursty and hierarchical structure in streams, in Proceedings of the 8th ACMSIGKDD International Conference on Knowledge Discovery and Data Mining, (Edmonton, Alberta, Canada, 2002), ACM Press, 2002, 91-101.
22. Klinkenberg, R. and Renz, I., Adaptive information filtering: learning in the presence of concept drifts, in Learning for Text Categorization, (Menlo Park, CA, 1998), AAAI Press, 1998, 33-40.
23. Kostoff, R.N. Systematic acceleration of radical discovery and innovation in science and technology. Technological Forecasting and Social Change, 73, (2006), 13.
24. Kumar, R., Novak, J., Raghavan, P. and Tomkins, A., On the Bursty Evolution of Blogspace. in WWW2003, (Budapest, Hungary, 2003), 2003, 477.
25. Kurtz, M.J., Eichhorn, G., Accomazzi, A., Grant, C.S., Demleitner, M. and Murray, S.S. Worldwide use and impact of the NASA Astrophysics Data System digital library. Journal of the American Society for Information Science and Technology, 56, (2005), 36-45.
26. Meadows, A.J. and O'Connor, J.G. Bibliographical statistics as a guide to growth points in science. Science Studies, 1, 1 (1971), 95-99.
27. Morinaga, S. and Yamanishi, K., Tracking dynamics of topic trends using a finite mixture model, in KDD'04, (Seattle, Washington, 2004), ACM, 2004, 811-816.
28. Newman, M., The structure of scientific collaboration networks, in Natl. Acad. Sci., (USA, 2001b), 2001b, 404-409.
29. Quinlan, R. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA., 1993.
30. Radicchi, F., Castellano, C., Cecconi, F., Loreto, V. and Parisi, D. Defining and identifying communities in networks arXiv: cond- mat/0309488 v1, 2003.
31. Root-Bernstein, R.S. Discovering: Inventing and solving problems at the frontiers of scientific knowledge. Harvard University Press, Cambridge, 1989.
32. Sidiropoulos, A., Katsaros, D. and Manolopoulos, Y. Generalized h-index for disclosing latent facts in citation networks. arXiv:cs.DL/0607066 (2006).
33. Simon, H.A., Langley, P.W. and Bradshaw, G.L. Scientific discovery as problem-solving. Synthese 47, (1981), 1-27.
34. Small, H. Visualizing science by citation mapping. Journal of the American Society for Information Science, 50, 9 (1999), 799-813.
35. Steyvers, M., Smyth, P., Rosen-Zvi, M. and Griffiths, T., Probabilistic author-topic models for information discovery, in KDD'04, (Seattle, Washington, 2004), ACM, 2004, 306-315.
36. Sullivan, D., Koester, D., White, D.H. and Kern, R. Understanding Rapid Theoretical Change in Particle Physics: A Month-By-Month Co-Citation Analysis. Scientometrics, 2, 4 (1980), 309-319.
37. Tabah, A.N. Literature dynamics: studies on growth, diffusion, and epidemics. Annual Review of Information Science and Technology, 34, (1999), 249-286.
38. Tsymbal, A., Pechenizkiy, M., Cunningham, P. and Puuronen, S. Dynamic integration of classifiers for tracking concept drift in antibiotic resistance data Technical Report TCD-CS2005-26, Department of Computer Science, Trinity College, Dublin, Ireland, 2005.
39. Wasserman, S. and Faust, K. Social Network Analysis: Methods and Applications. Cambridge University Press, 1994.
40. White, H.D. and McCain, K.W. Visualizing a discipline: An author co-citation analysis of information science, 1972-1995. Journal of the American Society for Information Science, 49, 4 (1998), 327-356.
41. York, D.G., Adelman, J., Anderson, J.E., Anderson, S.F., Annis, J., Bahcall, N.A. and al., e. The Sloan Digital Sky Survey: Technical summary. Astronomical Journal, 120, (2000), 1579-1587.
42. Zitt, M. and Bassecoulard, E. Delineating complex scientific fields by an hybrid lexical-citation method: An application to nanosciences. Information Processing & Management, 42, (2006), 18.