PageRank can be trivially calculated on commodity cluster hardware
The use of on-demand cloud computing infrastructure for data extraction and computation allows for scalability with increasing corpus size. In the event of increasing article burden, additional XML parsing nodes could be employed with linear cost and throughput. Despite the uncompressed corpus totalling approximately 40Gb, the fully citation-extracted form was <500 Mb. Therefore, we suggest that growth by an order of magnitude (in the range of entire MEDLINE database size) could still be stored on a single commodity hard drive.
Whilst the PageRank calculation was performed on a single node, expansion beyond 2Gb of RAM on a single computer is becoming cheaper and widely available [14]. The use of MapReduce for inverted citation network creation allows near-linear scalability, similar to XML parsing, and can thus be trivially re-evaluated as the corpus grows. PMC-OAS is updated daily, thus all metrics can be recalculated in a matter of minutes (minus the cost of data parsing), as required by the maintainer.
Expanding automated XML processing to MEDLINE as a whole is problematic
The PMC-OAS full-text articles are freely available in XML format, facilitating automated citation extraction. Unfortunately, the vast majority of MEDLINE articles are not open access, meaning that full-text access in not trivially available without bulk licencing programmes. Furthermore, the lack of XML-based metadata in non-open access articles limits the capability for rapid citation network generation.
Efforts have been made to parse bibliographic data from papers [15, 16], however attempts are limited by paid access to such articles in addition to the efficiency of extraction from a variety of article distribution file formats. We thus identify expansion beyond this 600,000-article training corpus as a major barrier to non-proprietary bibliometrics.
Articles appearing in PMC-OAS, referenced articles, which were not included in the corpus. This means that the latter’s PMID appeared in the citation network and thus received a PageRank. However, due to the limited inclusion set of this work, the PageRank (and thus relative ordering) is by no means final and would inevitably change should expansion to the whole of MEDLINE be feasible.
Other methods of importance quantification
Thus far, importance analysis has been derived from article citation networks alone. However, importance is a non-static entity, with the impact of papers going beyond that of, who cites who. Indeed, importance of a particular work may be represented by its spread through the scientific community, rather than an ‘acknowledgement-based’ system of the traditional publishing model. Social media may provide a real-time window into this community dissemination.
Altmetrics, the use of the social web for insight into article impact [17], has previously shown promise in correlation with citation count and may therefore add to bibliometrics through real-time importance weighting [18]. Consideration of social impact is beyond the scope of this research, though provides an exciting avenue for further exploration, perhaps in conjunction with PageRank.