A Heterogeneous Dynamical Graph Neural Networks Approach to Quantify Scientific Impact

• URL: http://arxiv.org/abs/2003.12042v1
• Date: Thu, 26 Mar 2020 17:15:36 GMT
• Title: A Heterogeneous Dynamical Graph Neural Networks Approach to Quantify Scientific Impact
• Authors: Fan Zhou, Xovee Xu, Ce Li, Goce Trajcevski, Ting Zhong, Kunpeng Zhang
• Abstract summary: We propose an approach based on Heterogeneous Dynamical Graph Neural Network (HDGNN) to explicitly model and predict the cumulative impact of papers and authors. Experiments conducted on a real citation dataset demonstrate its superior performance of predicting the impact of both papers and authors.
• Score: 39.9627229543809
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantifying and predicting the long-term impact of scientific writings or individual scholars has important implications for many policy decisions, such as funding proposal evaluation and identifying emerging research fields. In this work, we propose an approach based on Heterogeneous Dynamical Graph Neural Network (HDGNN) to explicitly model and predict the cumulative impact of papers and authors. HDGNN extends heterogeneous GNNs by incorporating temporally evolving characteristics and capturing both structural properties of attributed graph and the growing sequence of citation behavior. HDGNN is significantly different from previous models in its capability of modeling the node impact in a dynamic manner while taking into account the complex relations among nodes. Experiments conducted on a real citation dataset demonstrate its superior performance of predicting the impact of both papers and authors.

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