Structured Citation Trend Prediction Using Graph Neural Networks
This addresses the need for researchers and practitioners to identify future trends in academic fields, though it is incremental as it applies existing GNN advancements to citation prediction.
The paper tackled the problem of predicting top-cited papers at publication time using graph neural networks (GNNs) on academic citation graphs, and the result was that the proposed model outperformed other classic machine learning models in terms of F1-score.
Academic citation graphs represent citation relationships between publications across the full range of academic fields. Top cited papers typically reveal future trends in their corresponding domains which is of importance to both researchers and practitioners. Prior citation prediction methods often require initial citation trends to be established and do not take advantage of the recent advancements in graph neural networks (GNNs). We present GNN-based architecture that predicts the top set of papers at the time of publication. For experiments, we curate a set of academic citation graphs for a variety of conferences and show that the proposed model outperforms other classic machine learning models in terms of the F1-score.