A Graph Convolutional Neural Network based Framework for Estimating Future Citations Count of Research Articles

TL;DR

This paper introduces a GCN-based framework to predict future citation counts of research articles, aiding in assessing their impact over short and long-term periods using a large computer science dataset.

Contribution

It presents a novel GCN-based approach for citation prediction, specifically tailored for short-term and long-term forecasting of research article citations.

Findings

Effective prediction of future citations demonstrated.

Applicable to large-scale research datasets.

Outperforms baseline methods in citation estimation.

Abstract

Scientific publications play a vital role in the career of a researcher. However, some articles become more popular than others among the research community and subsequently drive future research directions. One of the indicative signs of popular articles is the number of citations an article receives. The citation count, which is also the basis with various other metrics, such as the journal impact factor score, the $h$-index, is an essential measure for assessing a scientific paper's quality. In this work, we proposed a Graph Convolutional Network (GCN) based framework for estimating future research publication citations for both the short-term (1-year) and long-term (for 5-years and 10-years) duration. We have tested our proposed approach over the AMiner dataset, specifically on research articles from the computer science domain, consisting of more than 0.8 million articles.