In a data-driven world, the amount of data currently collected and processed is perhaps the most spectacular result of the digital revolution. And the range of possibilities available has grown and will continue to grow. The Web is full of documents for humans to read, and with Semantic Web, data can also be understood by machines. W3C standardized RDF to represent the Web of data as modeled entities and their relations. Then SHACL came along to present constraints in RDF knowledge graphs, as a network of shapes. SHACL networks are usually presented in textual formats. This thesis focuses on visualizing SHACL networks in a 3D space, while providing many features for the user to manipulate the graph and get the desired information. Thus, SHACLViewer is presented as a framework for SHACL visualization. In addition, an evaluation for the impact of various parameters like network size, topology, and density are studied. During the study, execution times for different functions are computed; they include loading time, expanding the graph, and highlighting a shape. The observed results reveal the characteristics of the SHACL networks that affect the performance and scalability of SHACLViewer.