Descriptor: ViewPoints Network Graph Structure Analysis Dataset (VPN)

This article presents the ViewPoints Network (VPN) dataset, a fine-grained academic graph dataset designed to support the analysis of viewpoint-level structures within scientific literature. Existing academic graph datasets mainly operate at the paper or author level and therefore capture only coarse relational patterns such as citations or co-authorship, leaving the semantic evolution of scientific ideas insufficiently represented. The VPN dataset addresses this gap by extracting 1,474,680 viewpoints from publication abstracts and encoding them as individual nodes with high-dimensional sentence embeddings. Similarity-based edges are constructed using multiple thresholds, enabling flexible formation of viewpoint graphs tailored to different analytical needs. The dataset spans eight research domains in computer science and provides structured metadata, embedding files, and multi-threshold adjacency matrices to facilitate diverse graph learning and semantic analysis tasks. All data are processed through a transparent pipeline that includes cleaning, viewpoint extraction using large language models, embedding generation, and similarity computation. By offering a more granular representation of academic discourse, the VPN dataset supports research on viewpoint clustering, semantic interaction, idea evolution, and fine-grained network analysis across scientific domains.