Name
Abstract | ||
|---|---|---|
Quantifying and predicting the long-term impact of both scientific papers and individual authors have important implications for many academic policy decisions, from identifying emerging trends to assessing the merits of proposals for potential funding. This paper presents SI-HDGNN, a novel heterogeneous dynamical graph neural network that explicitly models a heterogeneous, weighted, directed and attributed academic graph, enabling a prediction of the cumulative scientific impact of papers and authors by a specifically designed aggregation method. Unlike the existing feature-based or homogeneous approaches, SI-HDGNN addresses the problem by capturing the temporal–structural characteristics of the papers and authors as well as their complex interactions and long-term dependencies. Extensive experiments conducted on three large-scale multidisciplinary academic datasets demonstrate its superior performance in predicting the long-term scientific impact of both scientific papers and authors. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.knosys.2021.107839 | Knowledge-Based Systems |
Keywords | DocType | Volume |
Scientific impact prediction,Heterogeneous information network,Graph neural network,Information diffusion,Science of science | Journal | 238 |
ISSN | Citations | PageRank |
0950-7051 | 0 | 0.34 |
References | Authors | |
39 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xu Xovee | 1 | 10 | 5.61 |
| Zhong Ting | 2 | 46 | 11.07 |
| Ce Li | 3 | 3 | 1.46 |
| Goce Trajcevski | 4 | 1732 | 141.26 |
| Fan Zhou | 5 | 101 | 23.20 |