Exploring Academic Influence of Algorithms by Co-occurrence Network Based on Full-text of Academic Papers
arXiv:2606.24099v1 Announce Type: new 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 per
The proliferation of AI and academic papers necessitates advanced methods to understand algorithm impact beyond isolated metrics, leading to this co-occurrence network analysis.
Understanding the interconnected influence of algorithms provides a more nuanced view of AI development, crucial for strategic R&D and investment in key technological areas.
This research provides a methodology for assessing collective algorithm influence, moving beyond individual metrics to highlight synergistic relationships and emergent trends in AI.
- · AI researchers
- · NLP developers
- · Academic institutions
- · Traditional citation metrics
Improved understanding of the evolution and interaction of algorithms in AI research.
More effective identification of impactful algorithm combinations leading to new breakthroughs.
Potential for early detection of emergent AI paradigms and shifts in research focus across the field.
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Read at arXiv cs.AI