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Can Scientific Publication's Network Structural Features Predict its Citation?
Conference proceeding

Can Scientific Publication's Network Structural Features Predict its Citation?

Zhuoran Luo, Jiangen He, Jiajia Qian, Yuqi Wang, Junying Chen, Wei Lu and Jialuo He
Proceedings of the ACM/IEEE Joint Conference on digital libraries in 2020, pp 485-486
01 Aug 2020
DOI: https://doi.org/10.1145/3383583.3398575

Abstract

citation prediction representation learning structural features
The citation relationship between scientific publications constitutes a huge and complex citation network, which is of great significance for hotspot analysis and cutting-edge prediction in different fields. Nevertheless, how to evaluate the novelty and impact of a scientific publication in its early stages remains an open question. To address this issue, we apply a network representation learning approach (struc2vec) to represent the full complexity of citation network structure, explore the extent to which an emerging science publication has changed the network structure of existing knowledge, and explain the relationship between this change and the paper's cited numbers from both clustering and network visualization perspectives. We found that the structural features captured by struc2vec can predict future citations of scientific publications to some extent. The predictive effects can be interpreted by how a new publication connects to and alters the existing structure of scientific knowledge in our visual analytics.

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