APPLYING NEXT GENERATION TOOLS, DATA, AND ECONOMIC COMPLEXITY IDEAS

Invited Talk by Matúš Medo "Network metrics for reputation and quality in scholarly data", Annual meeting of the German Physical Society (Regensburg, Germany)

10-03-2016
Location: 
Annual meeting of the German Physical Society, Regensburg

The ever-increasing quantity and complexity of scientific production have made it difficult for researchers to keep track of advances in their own fields. This, together with growing popularity of online scientific communities, calls for the development of effective information filtering tools. Network theory is an important driving aspect for such algorithms. We will first discuss the case of an online scientific community where users and papers form a bipartite network which can be effectively used to evaluate the reputation of users and fitness of papers. We show that when the input data is extended to a multilayer network including users, papers and authors, the resulting performance improves on multiple levels. In particular, top papers have higher citation count and top authors have higher h-index than top papers and top authors chosen by other algorithms. We will then move to stress the role of time in scholarly data. Most research metrics either ignore time (such as the h index) or consider it in an ad-hoc fashion (such as the m quotient). On the example of PageRank which has been used in the past to assess the quality of papers, we show that a demonstrably better ranking of papers can be obtained by considering time in a principled way.