This research paper introduces a novel method for analyzing the academic influence of algorithms within the field of Natural Language Processing (NLP). By constructing large-scale co-occurrence networks from the full text of academic papers, the study investigates how algorithms interconnect and influence each other over time. The analysis reveals that algorithms with high performance, those bridging different research eras, and those at the intersection of various subfields tend to exhibit greater influence. The paper highlights that declining algorithm influence is often preceded by a loss of central network position and weaker associations with other algorithms. AI
IMPACT Provides a new framework for understanding the evolution and impact of AI algorithms within academic research.
RANK_REASON The cluster contains academic papers detailing a new research methodology.
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