Leveraging GANs for citation intent classification and its impact on citation network analysis

• We propose a semi-supervised model for citation intent classification that integrates GANs with domain-specific SciBERT embeddings. • This paper applies intent-based filtering to large-scale citation networks, including the unarXiv dataset with 76k nodes and 171k edges. • cGAN-SciBERT yields competitive F1-scores (0.887 on SciCite) with a minimal number of parameters. • Citation intent filtering changes centrality rankings and enables a more refined assessment of paper influence. Citations play a fundamental role in the scientific ecosystem, serving as a foundation for tracking the flow of knowledge, acknowledging prior work, and assessing scholarly influence. In scientometrics, they are also central to the construction of quantitative indicators. Not all citations, however, serve the same function: some provide background, others introduce methods, or compare results. Therefore, understanding citation intent allows for a more nuanced interpretation of scientific impact. In this paper, we adopted a GAN-based method to classify citation intents. Our results revealed that the proposed method achieves competitive classification performance, closely matching state-of-the-art results with substantially fewer parameters. This demonstrates the effectiveness and efficiency of leveraging GAN architectures combined with contextual embeddings in an intent classification task. We also investigated whether filtering citation intents affects the centrality of papers in citation networks. Analyzing the network constructed from the unArXiv dataset, we found that paper rankings can be significantly influenced by citation intent. All four centrality metrics examined – degree, PageRank, closeness, and betweenness – were sensitive to the filtering of citation types. The betweenness centrality displayed the greatest sensitivity, showing substantial changes in ranking when specific citation intents were removed.