BIP! Ranker

BIP! Ranker is a software library, implemented in Apache Spark, that computes a set of citation-based impact indicators on top of scholarly knowledge graphs. In particular, it currently supports the following indicators (organised in categories based on the semantics of the impact aspect that they better capture):

• Influence indicators (i.e., indicators of the "total" impact of each research product; how established it is in general)
  • Citation Count: The total number of citations of the product, the most well-known influence indicator.
  • PageRank score: An influence indicator based on the PageRank [1], a popular network analysis method. PageRank estimates the influence of each product based on its centrality in the whole citation network. It alleviates some issues of the Citation Count indicator (e.g., two products with the same number of citations can have significantly different PageRank scores if the aggregated influence of the products citing them is very different - the product receiving citations from more influential products will get a larger score). 
• Popularity indicators (i.e., indicators of the "current" impact of each research product; how popular the product is currently)
  • RAM score: A popularity indicator based on the RAM [2] method. It is essentially a Citation Count where recent citations are considered as more important. This type of "time awareness" alleviates problems of methods like PageRank, which are biased against recently published products (new products need time to receive a number of citations that can be indicative for their impact).
  • AttRank score: A popularity indicator based on the AttRank [3] method. AttRank alleviates PageRank's bias against recently published products by incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to examine products which received a lot of attention recently.
• Impulse indicators (i.e., indicators of the initial momentum that the research product received right after its publication)
  • Incubation Citation Count (3-year CC): This impulse indicator is a time-restricted version of the Citation Count, where the time window length is fixed for all products and the time window depends on the publication date of the product, i.e., only citations 3 years after each product's publication are counted.

More details about the aforementioned impact indicators, the way they are calculated and their interpretation can be found here and in the respective references (e.g., in [4]).

You can find more details and full documentation in our GitHub repository page: https://github.com/athenarc/Bip-Ranker

References:

1. R. Motwani L. Page, S. Brin and T. Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report. Stanford InfoLab.
2. Rumi Ghosh, Tsung-Ting Kuo, Chun-Nan Hsu, Shou-De Lin, and Kristina Lerman. 2011. Time-Aware Ranking in Dynamic Citation Networks. In Data Mining Workshops (ICDMW). 373–380
3. I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Ranking Papers by their Short-Term Scientific Impact. CoRR abs/2006.00951 (2020)
4. I. Kanellos, T. Vergoulis, D. Sacharidis, T. Dalamagas, Y. Vassiliou: Impact-Based Ranking of Scientific Publications: A Survey and Experimental Evaluation. TKDE 2019 (early access)