Fig 1.
Algorithm of novelty computation.
Table 1.
Previous novelty measures.
Table 2.
Questionnaire of novelty.
Fig 2.
Distribution of distance and novelty.
The same sample for the third validation study (prediction of future citation) is used, except that oversampled highly-cited documents are excluded. The 947 selected documents include in total approximately 230,000 combinations of cited references, for which the distance (Eq 1) is computed (A). The distances are summarized at the focal document level (Eq 2), and Novel100 is displayed as an example (B). Novelty measures with different q values are illustrated in S1 Appendix. Since abstracts and keywords are not available for all documents, the sample sizes are smaller.
Table 3.
Validation of distance measures.
Fig 3.
Correlation between bibliometric and self-reported novelty measures.
Pearson’s correlation coefficient. Novelq (q∈{100,99,95,90,80,50}) is correlated with the mean of four self-reported newness scores (Column 9 in Table 4). †p<0.1, *p<0.05, **p<0.01, ***p<0.001.
Table 4.
Validation of novelty measures.
Table 5.
Odds ratio of top-1% citation rank.
Fig 4.
Prediction of top-1% citation rank.
The probability of a focal document falling within the top 1 percentile is predicted. For easier interpretation and comparison, the horizontal axis takes the percentile of the novelty measures. (A) based on Row 1 in Table 5. (B) and (C) based on curvilinear models incorporating the quadratic term of the novelty measures (S1 Appendix).