Exploring Academic Influence of Algorithms by Co-occurrence Network Based on Full-text of Academic Papers

This study presents a large-scale analysis of algorithm co-occurrence networks in natural language processing (NLP), utilizing deep learning models to extract algorithm entities from over four decades of academic papers. By constructing cumulative and annual co-occurrence networks, the research reveals structural characteristics and centrality measures that highlight the collective influence of algorithms, showing that classic and interdisciplinary algorithms maintain high centrality and popularity. This work lays the groundwork for understanding algorithmic influence in a network context, which is crucial for practitioners aiming to navigate the evolving landscape of AI research and applications.