Research topic trend prediction of scientific papers based on spatial enhancement and dynamic graph convolution network

TL;DR

This paper introduces a novel deep neural network model combining graph convolutional networks and temporal convolutional networks to accurately predict future research topic trends by capturing spatial dependencies and temporal dynamics.

Contribution

The paper presents a new spatiotemporal convolutional network model that effectively models dependencies between research themes for trend prediction, outperforming existing methods.

Findings

Model outperforms state-of-the-art baselines in research topic prediction.

Effectively captures spatial dependencies between research themes.

Accurately models temporal dynamics of research trends.

Abstract

In recent years, with the increase of social investment in scientific research, the number of research results in various fields has increased significantly. Accurately and effectively predicting the trends of future research topics can help researchers discover future research hotspots. However, due to the increasingly close correlation between various research themes, there is a certain dependency relationship between a large number of research themes. Viewing a single research theme in isolation and using traditional sequence problem processing methods cannot effectively explore the spatial dependencies between these research themes. To simultaneously capture the spatial dependencies and temporal changes between research topics, we propose a deep neural network-based research topic hotness prediction algorithm, a spatiotemporal convolutional network model. Our model combines a graph convolutional neural network (GCN) and Temporal Convolutional Network (TCN), specifically, GCNs are used to learn the spatial dependencies of research topics a and use space dependence to strengthen spatial characteristics. TCN is used to learn the dynamics of research topics' trends. Optimization is based on the calculation of weighted losses based on time distance. Compared with the current mainstream sequence prediction models and similar spatiotemporal models on the paper datasets, experiments show that, in research topic prediction tasks, our model can effectively capture spatiotemporal relationships and the predictions outperform state-of-art baselines.