1. “The Rise and Rise of Citation Analysis”
Tanmoy Chakraborty
CNeRG, IIT-Kgp, India

2. Twofold Research Interests
Analyzing communities/clusters in complex networks
Studying different aspects of citation network

3. “The Rise and Rise of Citation Analysis”
In collaboration with
Suhansanu Kumar, Pawan Gowel,
Animesh Mukherjee, Niloy Ganguly

4. Mixed Sentiment Sense and non-sense about citation analysis (*6860)
--- T. Opthof, Cardiovascular research, 97
The rise and rise of citation analysis (*1399)
--- Lokman I. Meho, Phy. Res., 07
Does citation pay? (*887)
--- Fowler & Aksnes, Scientometrics, 07
Think beyond citation analysis (*1009)
--- Sarli et al., NIPS, 10

5. Sooner or later, you will definitely be subjected to such an analysis
Raw Citations Count
To assess
Quality of a paper
Prominence of a researcher
Success of a collaboration/group
Quality of a conference/journal
Quality of an Institute
Impact of a research area
Sooner or later, you will definitely be subjected to such an analysis
Only Citation
Count

6. Bibliometrics: Raw Citation Count
Journal Impact factor
Immediacy factor
Eigen factor
Altmetric
5 years Impact factor
Common assumption
Citation

7. Publication Universe Crawled entire Microsoft Academic Search
Papers only in Computer Science domain
Basic preprocessing
Basic Statistics of papers from
Values
Number of valid entries
3,473,171
Number of authors
1,186,412
Number of unique venues
6,143
Avg. number of papers per author
5.18
Avg. number of authors per paper
2.49

8. Available Metadata for each paper
Publication Universe
Available Metadata for each paper
Title
Unique ID
Named entity disambiguated authors’ name
Year of publication
Named entity disambiguated publication venue
Related research field(s)
References
Keywords
Abstract
Citation context

9. Citation Profile An exhaustive analysis of the citation profiles
Papers having at least 10 yrs history and consider at most 20yrs history
Scale the entries of the citation profile between 0-1
Use peak-detection heuristics
Each peak should be at least 75% of the max peak
Two consecutive peak should be separated at least 3 yrs

10. Five Universal Citation Profiles
Avg. behavior
Q1 and Q3 represent the first and third quartiles of the data points respectively.
Another category: ‘Oth’ => having less than one citation (on avg) per year

11. Five Universal Citation Profiles A deeper look

12. Immediate Questions
Is the Journal Impact factor (JIF) formula correct?
JIF at year 2000 : Eugene Garfield (1975)
# of citations received in 2000 by papers published in that journal in 1998 and 1999
divided by
# of papers published in the journal in 1998 and 1999

13. What does JIF really imply?
Immediate Questions
What does JIF really imply?
Importance of the recent papers in current time period?
Relevance of the journal itself in current time period
Why not all the citations at current time
Why last 2 years?

14. Immediate Questions
JIF overlooks the importance of Peak_Late and MonIncr
Over-consider
Over-consider
Under-consider

15. More on the Categories
Are they biased on the year of publication? (Aging factor)
Ans: No
Same age
Mean year
Year deviation
Peak_Int
1994
5.19
Peak_Mul
1992
6.68
Peak_Late
6.54
MonDec
5.43
MonIncr
1993
5.36

16. More on the Categories 2. Are they biased on Journals/conferences?
Peak_Int
Peak_Mul
Peak_Late
MonDec
MonIncr
% of conferences paper 65
39.03
39.89
60.73
25.26
% of Journal paper 35
60.97
60.11
39.27
74.74

17. More on the Categories 3. Are they affected by self-citation?
Transition matrix showing the transition of categories after
removing self citations
Least affected by self citations
Peak_Int
Peak_Mul
Peak_Late
MonDec
MonIncr
Oth
0.72
0.10
0.03
0.01
0.15
0.02
0.81
0.04
0.11
0.06
0.86
0.05
0.14
0.41
0.35
0.88
0.09
Most affected by self citation

18. More on the Categories 4. What about Peak_Mul? Avg Peak Height
Peak_Int
5.1
Time
4.2
Peak_Mul
= 5.6
Avg Peak Height
( ) = 6.8
~ ( )
3.1
2.5
5.3
12.1
Peak_Late
5.3
10.8
Years after publication
Peak_Mul Might be Intermediary between Peak_Int and Peak_Late

19. Where does this classification help?
To improve Bibliometrics in scientific research
Various prediction systems
Future citation prediction system
Predicting emerging field/topic
Predating future star/seminal papers
Paper search and Recommender systems

20. at the Time of Publication
On predicting
Future Citation Count
at the Time of Publication

21. Problem Definition

22. Traditional Framework
Yan et al., JCDL, 2012

23. Problems in Traditional Framework
Consider initial few years’ statistics after publication
Proved to be very effective
Lack of time dimension in prediction
Suffers a lot from outlier points during regression

24. Problems in Traditional Framework: how to tackle
Consider initial few years’ statistics after publication
Proved to be very effective
Lack of time dimension in prediction
Suffers a lot from outlier points during regression
Try to predict citations as early as possible
(may be at the time of publication )
Should consider the time dimension
Reduce outlier points as much as possible

25. Our Framework: 2-stage Model

26. Features Author-centric (Max/Avg) Productivity H-index Versatility
Sociality
Venue-centric
Prestige
Impact Factor
Paper-centric
Team-size
Reference count
Reference diversity
Keyword diversity
Topic diversity

27. Performance Evaluation
Coefficient of determination (R2)
The more, the better
Mean squared error (θ)
The less, the better
Pearson correlation coefficient (ρ) The more, the better

28. Performance of SVM
Confusion Matrix

29. Performance of Regression Model

30. Feature Analysis

31. Conclusion Five universal citation profiles
Different analysis on these categories
Can help to reframe existing bibliometrics
Can be a generic way in machine learning
Can enhance the performance of the existing systems

32. Thank You