2026-06-24 04:00 UTCOriginal source2 min readUpdated: 2026-06-24 07:44 UTC

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 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.

SourcearXiv AIAuthor: Yuzhuo Wang, Chengzhi Zhang, Min Song, Seong Deok Kim, Youngsoo Ko, Juhee Lee

[2606.24099] Exploring Academic Influence of Algorithms by Co-occurrence Network Based on Full-text of Academic Papers

[Submitted on 23 Jun 2026]

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

View a PDF of the paper titled Exploring Academic Influence of Algorithms by Co-occurrence Network Based on Full-text of Academic Papers, by Yuzhuo Wang and 5 other authors

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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.

Subjects:

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Digital Libraries (cs.DL); Information Retrieval (cs.IR)

Cite as: arXiv:2606.24099 [cs.AI]

(or arXiv:2606.24099v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.24099

arXiv-issued DOI via DataCite (pending registration)

Journal reference: aslib JIM, 2025

Related DOI:

https://doi.org/10.1108/AJIM-09-2023-0352

DOI(s) linking to related resources

Submission history

From: Chengzhi Zhang [view email] [v1] Tue, 23 Jun 2026 03:26:39 UTC (1,706 KB)

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