Two citation-based indicators to measure latent referential value of papers

Traditional bibliometric indicators aim at helping academic administrators or research investors measure the influence of publications. These indicators focus on how to quantify and compare the scientific output of researchers. However, little attention has been paid to the aspect that bibliometric indicators can also be used to help scientists find valuable referential papers. In this paper, we propose three points to characterize valuable referential papers: first, valuable referential papers always are high-quality research; second, valuable referential papers are closely related to a considerable quantity of recent papers; third, valuable referential papers lead to hotspots which attract papers to follow successively. We extract the critical subnetwork from the original citation network which only reserves the significant nodes and edges that meet the three preceding points. Then we present two indicators on the basis of the critical subnetwork. The experimental results demonstrate that papers recommended by our indicators are relatively new and our indicators have greater Spearman's rank correlation coefficients with the future citation count compared with other bibliometric indicators like the raw citation count.