Short (25 minute) talk from the International Symposium on Science of Science

1. International Symposium on the Science of Science
Library of Congress
March 21st 2016
Citation networks and the stories they tell
CarlT. Bergstrom
University of Washington

2. Jevin West Martin Rosvall
SciSIP
Jennifer Jacquet Jacob Foster Shelley Correll
Molly King DarilVilhena Ted Bergstrom
James Evans Ben Althouse Moritz Stefaner
Daniel Edler Ian Wesley-Smith Rodney Garrett
Michael Jensen Morton Bech Ralph Dandrea
Gregg Gordon

3. Eigenfactor.org/projects/well-­‐formed/
Citation is a core institution of
academic science, tracing the flow of
ideas over time.

4. The sum of all citations create a vast
network of more than a billion citations
among more than 100 million papers
Eigenfactor.org/projects/well-­‐formed/

5. Every one of those citations represents
a careful decision by domain experts.
Eigenfactor.org/projects/well-­‐formed/

6. The citation network of science holds a
wealth of information about how science
works, and how it can work better.
Eigenfactor.org/projects/well-­‐formed/

7. How can we extract
this information?
Eigenfactor.org/projects/well-­‐formed/

9. The first step is to assemble the data.
We have compiled citation networks
from many sources:

10. How important is any particular
paper, or any particular journal,
in the network?
Mapequation.org

11. Count incoming links
(Impact Factor)
Mapequation.org

12. Count incoming links
(Impact Factor)
Use the whole network
(Eigenfactor)
Mapequation.org

13. Important websites
are linked to by
important websites.

14. Important papers
are cited by
important papers
Important journals
are cited by
important journals

15. Eigenfactor algorithm
P = α H + (1 − α) a.eT
Matrix representing the
random walk over citations Probability of
not teleporting
Cross-citation Matrix
dictating the structure
of the citation network
Probability of teleporting
to completely new journal
weighted by the number
of articles in that journal
EF =100
Hπ
[Hπ]ii
∑
Leading eigenvector
of the random walk
matrix P.
Normalization
Bergstrom (2007);West et al (2010)

16. Applet coding: Daniel Edler
Mapequation.org

17. The Eigenfactor Algorithm

21. Study, and publicize, the cost-
effectiveness of journal subscriptions
Eigenfactor.org
Bergstrom and Bergstrom 2004 PNAS

22. Study, and publicize, the cost-
effectiveness of open access publishing
Eigenfactor.org

23. Ranking authors
“Author-level Eigenfactor
performs best in identifying
high-impact authors”
- Dunaiski et al.
J. Informetrics May 2016
West et al 2013 JASIST

24. Ranking articles:
The Article-Level Eigenfactor (ALEF) Algorithm
Time
Older
papers
Newer
papers
Wesley-Smith et al 2016; West et al in prep.

26. Image Courtesy of Mark Newman
Small networks reveal structure
directly.
Dating network
in a Michigan
high school

27. Large networks could use some
assistance.
Yeast protein
interaction network
Ho et al. (2002) Nature

28. good maps simplify
and highlight
relevant structures
Boston MTAGoogle maps

29. Network community detection
We want a modular description of a
weighted, directed network:
Most flow on the network occurs within,
not between, local modules.

30. Data
Compressing Finding patterns
If we can find a good code for describing flow on a
network, we will have solved the dual problem of
finding the important structures with respect to
that flow.

31. The map equation tells us the description
length for a particular modular structure
The map equation

32. We conclude that the infomap method by
Rosvall and Bergstrom is the best performing…
Among other things, the method can be
applied to weighted and directed graphs as
well, with excellent performances, so it has a
large spectrum of potential applications.”
- Lancichinetti and Fortunato (2009)
“

33. Rosvall and Bergstrom (2008) PNAS

34. Althouse et al (2009) JASIST

35. “coverage” Impact factor
Althouse et al (2009) JASIST

36. 1995 2004

37. 1. Determine which structures
are statistically significant.
2. Visualize changes in those
structures.

38. Rosvall and Bergstrom (2010)
PLoS One
The emergence of neuroscience

39. The map equation tells us the description
length for a particular hierarchical structure
The hierarchical map equation

40. Rosvall and Bergstrom (2011)
PLoS One

41. Rosvall and Bergstrom (2011) PLoS One
Revealing hierarchical structure

42. Rosvall and Bergstrom (2011) PLoS One
Revealing hierarchical structure

43. Rosvall and Bergstrom (2011) PLoS One
Revealing hierarchical structure

44. Rosvall and Bergstrom (2011) PLoS One
Revealing hierarchical structure

45. Rosvall and Bergstrom (2011)
PLoS One
Revealing hierarchical structure

46. Using hierarchical structure for
scholarly recommendation
West et al (2016) In press

47. http://babel.eigenfactor.org
Using hierarchical structure for
scholarly recommendation

48. 1920 1940 1960 1980 2000
0.100.150.200.250.30
percentageofwomen
What gender disparities still exist across academia?
first author
West et al. 2013 PLoS One

49. 1920 1940 1960 1980 2000
0.100.150.200.250.30
percentageofwomen
What gender disparities remain
in scholarly publishing?
last author
first author
West et al. 2013 PLoS One

50. Eigenfactor.org

51. Self-citation rate by gender
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Women's and men's rates of self-citation
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Based on 3 million papers from JSTOR
King et al. in prep.

52. Rates of rates of self-citation do make a
difference to impact metrics, particularly the h-index.
– Cameron et al 2016 Bioscience
Self-citations per authorship
”
“
King et al. in prep.