This article introduces a novel method for progressively visualizing the evolution of a knowledge domain's cocitation network. The method involves creating a sequence of cocitation networks from equal-length time interval slices, which are then merged and visualized in a panoramic view to identify visually salient features that indicate intellectually significant articles. The method is applied to a study of the superstring field in theoretical physics, focusing on identifying articles that triggered two superstring revolutions. Visually salient nodes in the panoramic view are validated by leading scientists in the field, demonstrating that intellectual turning points can be identified through these nodes. The primary goal of knowledge domain visualization (KDViz) is to detect and monitor the evolution of a knowledge domain. Progressive KDViz focuses on techniques to identify temporal patterns associated with significant contributions as a domain advances. Scientific networks, including citation, coauthorship, and cocitation networks, are dynamic and change over time. Understanding these changes is essential for researchers in a scientific field. The method uses a divide-and-conquer strategy, dividing time intervals into slices and deriving cocitation networks from each. These networks are merged and visualized to highlight major changes between adjacent slices. The visualization simplifies the search for significant papers by focusing on visually salient features such as landmark, hub, and pivot nodes. The entire process is implemented in a computer system called CITESPACE. The method addresses three key issues: improving network clarity, merging heterogeneous networks, and identifying visually salient nodes. It uses Pathfinder network scaling to reduce link crossings and improve clarity. The method also addresses merging networks by identifying persistent and new elements between adjacent networks. Visually salient nodes are crucial for identifying intellectual turning points. The method was tested on the superstring field, where it successfully identified key articles that triggered revolutions. The results showed that visually salient nodes can be used to identify significant contributions, and the method provides a practical way to visualize and monitor the evolution of scientific knowledge. The method has potential applications beyond the superstring field, offering a promising approach for studying the evolution of scientific networks. The study highlights the importance of domain experts in validating and interpreting visualizations, and the method's ability to detect intellectual turning points through visual features. The method is a valuable tool for scientists to understand and monitor the evolution of their own fields.This article introduces a novel method for progressively visualizing the evolution of a knowledge domain's cocitation network. The method involves creating a sequence of cocitation networks from equal-length time interval slices, which are then merged and visualized in a panoramic view to identify visually salient features that indicate intellectually significant articles. The method is applied to a study of the superstring field in theoretical physics, focusing on identifying articles that triggered two superstring revolutions. Visually salient nodes in the panoramic view are validated by leading scientists in the field, demonstrating that intellectual turning points can be identified through these nodes. The primary goal of knowledge domain visualization (KDViz) is to detect and monitor the evolution of a knowledge domain. Progressive KDViz focuses on techniques to identify temporal patterns associated with significant contributions as a domain advances. Scientific networks, including citation, coauthorship, and cocitation networks, are dynamic and change over time. Understanding these changes is essential for researchers in a scientific field. The method uses a divide-and-conquer strategy, dividing time intervals into slices and deriving cocitation networks from each. These networks are merged and visualized to highlight major changes between adjacent slices. The visualization simplifies the search for significant papers by focusing on visually salient features such as landmark, hub, and pivot nodes. The entire process is implemented in a computer system called CITESPACE. The method addresses three key issues: improving network clarity, merging heterogeneous networks, and identifying visually salient nodes. It uses Pathfinder network scaling to reduce link crossings and improve clarity. The method also addresses merging networks by identifying persistent and new elements between adjacent networks. Visually salient nodes are crucial for identifying intellectual turning points. The method was tested on the superstring field, where it successfully identified key articles that triggered revolutions. The results showed that visually salient nodes can be used to identify significant contributions, and the method provides a practical way to visualize and monitor the evolution of scientific knowledge. The method has potential applications beyond the superstring field, offering a promising approach for studying the evolution of scientific networks. The study highlights the importance of domain experts in validating and interpreting visualizations, and the method's ability to detect intellectual turning points through visual features. The method is a valuable tool for scientists to understand and monitor the evolution of their own fields.