Data labeling through the centralities of co-reference networks improves the classification accuracy of scientific papers

Zheng Xie, Yiqin Lv, Yiping Song, Qi Wang

Abstract: Highlights•A model-agnostic optimization objective is proposed to improve paper classification accuracy.•The optimization objective is maximizing a paper set's neighborhood in a co-reference network.•Labeling high-centrality nodes is a labor-saving and approximative way to the maximizing problem.