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

Published: 2026-06-24 · Updated: 2026-06-24 · Category: Research

Source page: https://www.sipply.dev/story/exploring-academic-influence-of-algorithms-by-co-occurrence-b30d0476 · 中文: https://www.sipply.dev/zh/story/exploring-academic-influence-of-algorithms-by-co-occurrence-b30d0476

## Summary

A recent study published on arXiv investigates the co-occurrence networks of algorithms within the field of natural language processing (NLP) by analyzing the full text of academic papers. The research uncovers how the popularity and influence of various algorithms have changed over time, providing insights into the structural characteristics of these networks. By employing centrality measures, the study illustrates the interconnectedness of algorithms and their collective impact on the evolution of NLP research.

## Key takeaways

- The study provides a comprehensive analysis of algorithm influence in NLP through co-occurrence networks.
- It highlights the changing dynamics of algorithm popularity over time.
- Centrality measures are used to understand the structural characteristics of algorithm networks.

## Why it matters

This research sheds light on the academic landscape of algorithms, helping to identify key players and trends in NLP. Understanding these dynamics can inform future research directions and collaborations.

- **Product managers:** For product managers, this study offers valuable insights into which algorithms are gaining traction, aiding in strategic decision-making for product development and feature prioritization.
- **Developers:** Developers can leverage the findings to stay informed about trending algorithms and their applications, ensuring they are using the most relevant and effective tools in their projects.
- **Students and job seekers:** Students and job-seekers can benefit from understanding the evolving landscape of NLP algorithms, positioning themselves to acquire skills in the most influential and relevant technologies in the field.

## Sources

- [Exploring Academic Influence of Algorithms by Co-occurrence Network Based on Full-text of Academic Papers](https://arxiv.org/abs/2606.24099) (arXiv cs.AI, 2026-06-24)

## 中文

### 基于学术论文全文的算法共现网络学术影响力探索

最近在arXiv上发表的一项研究通过分析学术论文的全文，调查了自然语言处理（NLP）领域中算法的共现网络。研究揭示了各种算法的流行度和影响力如何随时间变化，为这些网络的结构特征提供了洞察。通过采用中心性度量，该研究展示了算法之间的相互联系及其对NLP研究演变的集体影响。

**要点：**

- 该研究通过共现网络提供了对NLP中算法影响力的全面分析。
- 它突出了算法流行度随时间变化的动态。
- 使用中心性度量来理解算法网络的结构特征。

**为什么重要：** 这项研究揭示了算法的学术格局，有助于识别NLP中的关键参与者和趋势。理解这些动态可以为未来的研究方向和合作提供信息。

---
Sipply: Sip AI News Simply. https://www.sipply.dev