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

2026-06-22

AI Summary

This study addresses the limitation of existing research that typically evaluates algorithmic impact in isolation, neglecting their interdependent effects within the broader research ecosystem. For the first time, we construct a co-occurrence network of natural language processing algorithms based on full-text academic papers, integrating deep learning–based entity recognition with complex network analysis to systematically characterize the collective, cumulative, and annual evolution of algorithmic influence. Our analysis reveals that this network exhibits hallmark properties of complex systems: high-centrality positions are consistently occupied by classic high-performance algorithms and those bridging multiple time periods. Furthermore, algorithmic influence decay follows a distinct pattern—loss of core structural position precedes the weakening of associative ties. This work establishes a foundational framework for understanding the structural interplay among algorithms, researchers, and tasks.

Abstract

Algorithms have become central to scientific research in the era of artificial intelligence (AI). Although algorithm mentions in papers are often used to indicate popularity and influence, existing studies usually evaluate individual algorithms in isolation and pay limited attention to the collective influence formed through their interconnections. This study constructs large-scale algorithm co-occurrence networks in natural language processing (NLP) based on the full text of academic papers and investigates algorithm influence from a network perspective. Using deep learning models, we extract algorithm entities and build overall, cumulative, and annual co-occurrence networks. We analyze their structural characteristics and apply multiple centrality measures to assess the group influence of algorithms across the whole field and over time. The results show that algorithm networks display typical features of complex networks, with increasingly dense connections developing over approximately two decades. Classic, high-performing algorithms and those located at the intersections of different research periods tend to have high popularity, control, centrality, and balanced influence. When the influence of an algorithm declines, it usually loses its core network position first, followed by weaker associations with other algorithms. This study is the first large-scale analysis of algorithm co-occurrence networks. Covering more than four decades of academic publications, it provides a temporal and structural view of algorithm influence and offers a foundation for future research on networks linking algorithms, scholars, and tasks.

Problem

• algorithm influence
• co-occurrence network
• academic papers
• collective influence
• network analysis

Innovation

• algorithm co-occurrence network
• deep learning-based entity extraction
• network centrality analysis
• temporal influence dynamics
• collective algorithm influence