Graph-structured data appears across scientific, social, biological, and information systems, but extracting useful and reproducible representations from networks remains a practical challenge. NEExT provides a workflow for preparing graph data, computing structural features, generating graph embeddings, and supporting exploratory analysis. This work focuses on embedding-based graph analysis as a bridge between raw network structure and downstream interpretation, allowing users to compare graphs, examine structural variation, and connect computational patterns to domain-level questions.