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Transforming Citation Networks into Insights: Mapping Scholarly Influence with Advanced Graph Models
Research Article | Published: 04 October 2025
ICCK Transactions on Advanced Computing and Systems Volume 1, Issue 4, 2025: 238-257
Volume 1, Issue 4, ICCK Transactions on Advanced Computing and Systems
Volume 1, Issue 4, 2025
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ICCK Transactions on Advanced Computing and Systems, Volume 1, Issue 4, 2025: 238-257

Open Access | Research Article | 04 October 2025
Transforming Citation Networks into Insights: Mapping Scholarly Influence with Advanced Graph Models
by
Tahir Sher Tahir Sher 1
Abdul Rehman Abdul Rehman 2 *
Imran Ihsan Imran Ihsan 3
1 Department of Artificial Intelligence, Korea University, Seoul 02842, Republic of Korea
2 Convergence Institute of Human Data Technology, Jeonju University, Jeonju 55069, Republic of Korea
3 Department of Creative Technologies, Air University, Islamabad 44000, Pakistan
* Corresponding Author: Abdul Rehman, [email protected]
DOI: 10.62762/TACS.2025.939169
Received: 14 May 2025, Accepted: 09 July 2025, Published: 04 October 2025  
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Abstract
The growing role of citation relations in identifying research impact has spurred much investigation on assessing the most cited papers and their roles within datasets. Due to the richness of the CORA dataset, this study selects highly cited papers and measures the results of node classification, as well as the H-index of research articles. Besides, it explores the correlations and robustness with regard to the nodes by computing their chances and studying their connections. To these ends, linear transformation was utilized for mapping low-level node features to high-level, and the Graph Attention Networks (GAT) for node classification. The study was able to find highly cited papers and compute their H-index, which gives insight into the citation patterns in the CORA dataset. For instance, Paper ID 12182 reported an H-index of 20, while a high citation paper index of 35 received 166 citations. On the test dataset, the study achieved a node classification accuracy ranging from 78.6% to 82% and an F1 score of 78.14%. Furthermore, 7 nodes of the machine learning domain have also been distinguished and categorized according to their features and their relations within the graph. In the citation network identified, the present research detailed the citation interconnection that characterized the works within the dataset. Our research mainly focuses on new tasks such as the extraction of highly cited papers and the calculation of the H index that improve the comprehension of the scholarly influence and the citation relation for future development strategies in citation network analysis.
Graphical Abstract
Transforming Citation Networks into Insights: Mapping Scholarly Influence with Advanced Graph Models
Keywords
deep learning
text mining
graph attention networks
citation
h-index
Data Availability Statement
The code supporting the findings of this study is openly available on GitHub at https://github.com/TahirSher/Graph-Attention-Networks-for-Node-Classification-and-Highly-Cited-Papers. All experiments were conducted using Google Colab with Python~3.10 and standard library versions compatible with Python~3.10.
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
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APA Style
Sher, T., Rehman, A., & Ihsan, I. (2025). Transforming Citation Networks into Insights: Mapping Scholarly Influence with Advanced Graph Models. ICCK Transactions on Advanced Computing and Systems, 1(4), 238–257. https://doi.org/10.62762/TACS.2025.939169
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ICCK Transactions on Advanced Computing and Systems

ICCK Transactions on Advanced Computing and Systems

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