Abstract
Link prediction implies the mining of the missing links in networks or prediction of the next node pair to be connected by a link. Link prediction is useful for mining information in citation networks, and most of the existing related studies commonly use degree rather than more advanced methods to measure the importance of nodes. However, such a method cannot easily measure the importance of a paper in reality; some papers have high degree in citation networks but are not very influential. This issue restricts the performance of the link prediction methods applied to citation networks. The current study analyzed h-type indices, which are more suitable than degree for measuring the importance of citation network nodes. We propose two h-index-based link prediction methods. Experiments conducted on real citation networks demonstrate that the use of h-type index to measure the importance of nodes in citation networks can significantly improve the prediction accuracy of link prediction methods.
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This work is supported by National Natural Science Foundation of China (NSFC No. 71203135).
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Zhou, W., Gu, J. & Jia, Y. h-Index-based link prediction methods in citation network. Scientometrics 117, 381–390 (2018). https://doi.org/10.1007/s11192-018-2867-7
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DOI: https://doi.org/10.1007/s11192-018-2867-7