Measuring influence in scientific fields

What can citation data reveal about the flow of influence in academia?

By and

This interactive visualisation represents 29 million citations exchanged between 11,000 academic journals in 2006–2015. Journals are ranked against each other using a pairwise comparison model: for any pair of journals, the publication with the higher influence score is more likely to receive citations than to give them away.

Fields have been generated algorithmically from citation data using the Infomap algorithm. By aggregating the journals in each community into a single ‘superjournal’ we can model the exchange of citations between disciplines. Fields with higher influence scores are more likely to be cited by other fields. Click on a field to see an intra-field ranking of the constituent journals. The error bars are 95% comparison intervals computed from quasi-variances.

‘Wider influence’ is calculated in the same way as intra-field influence, but with the inclusion of an ‘Other Fields’ superjournal of aggregated citations to and from journals outside the field. Higher scores reflect interdisciplinary influence—some publications are influential within their community but not outside it and vice versa. The Other Fields superjournal's own score indicates how insular or interdisciplinary the community is as a whole.

Inter-field influence ranking