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

arXiv: https://arxiv.org/abs/2606.24099 · HTML: https://mlsanity.com/paper/2606.24099/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 97 references that are in the map.
- First posted on arXiv 23 Jun 2026.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Portuguese Named Entity Recognition using BERT-CRF](https://mlsanity.com/paper/1909.10649/) (2019)
- [Using the Full-text Content of Academic Articles to Identify and Evaluate Algorithm Entities in the Domain of Natural Language Processing](https://mlsanity.com/paper/2010.10817/) (2020)
- [Entitymetrics: Measuring the Impact of Entities](https://mlsanity.com/paper/1309.2486/) (2013)
- [A Review on Method Entities in the Academic Literature: Extraction, Evaluation, and Application](https://mlsanity.com/paper/2209.03687/) (2022)
- [Method and Dataset Entity Mining in Scientific Literature: A CNN + Bi-LSTM Model with Self-attention](https://mlsanity.com/paper/2010.13583/) (2020)
- [Recurrent neural network models for disease name recognition using domain invariant features](https://mlsanity.com/paper/1606.09371/) (2016)
- [Data objects and documenting scientific processes: An analysis of data events in biodiversity data papers](https://mlsanity.com/paper/1903.06215/) (2019)
- [Evolution of interdependent co-authorship and citation networks](https://mlsanity.com/paper/1909.00185/) (2019)

## Abstract (arXiv)

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.