Abstract
Measuring and evaluating an author’s impact has been a withstanding challenge in the academic world with profound effects on society. Apart from its practical usage for academic evaluation, it enhances transparency and reinforces scientific excellence. In this demo paper we present our efforts to address this problem capitalizing on the field-based citations and the author oriented citation network extracted from the Microsoft Academic Graph, to our knowledge the largest network of its kind. We separate impact into two dimensions: success and influence over the network, and provide two novel scientometrics to quantify some of their aspects: (i) the distribution of the h-index for specific scientific fields and a search engine to visualize an authors’ position in it as well as the top percentile she belongs to, (ii) recomputing our previously introduced D-core influence metric on this huge network and presenting authority/integration of the authors in the form of D-core frontiers. In addition we present interesting insights on the most dense scientific domains and the most influential authors. We believe the proposed analytics highlight under-examined aspects in the area of scientific evaluation and pave the way for more involved scientometrics.
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Notes
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Created at 9/2/2019.
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All the queries were performed in PySpark in a cluster of 32 nodes, 16Â GB ram each, Intel(R) Xeon(R) CPU E5-2407 v2 @ 2.40Â GHz.
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Panagopoulos, G., Xypolopoulos, C., Skianis, K., Giatsidis, C., Tang, J., Vazirgiannis, M. (2020). Scientometrics for Success and Influence in the Microsoft Academic Graph. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 882. Springer, Cham. https://doi.org/10.1007/978-3-030-36683-4_80
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