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Quantifying and predicting the long-term impact of scientific writings orindividual scholars has important implications for many policy decisions, suchas funding proposal evaluation and identifying emerging research fields. Inthis work, we propose an approach based on Heterogeneous Dynamical Graph NeuralNetwork (HDGNN) to explicitly model and predict the cumulative impact of papersand authors. HDGNN extends heterogeneous GNNs by incorporating temporallyevolving characteristics and capturing both structural properties of attributedgraph and the growing sequence of citation behavior. HDGNN is significantlydifferent from previous models in its capability of modeling the node impact ina dynamic manner while taking into account the complex relations among nodes.Experiments conducted on a real citation dataset demonstrate its superiorperformance of predicting the impact of both papers and authors.

A Heterogeneous Dynamical Graph Neural Networks Approach to Quantify Scientific Impact