The GRAPHNET Network Science and Graph Analytics Platform provides dedicated computational resources and professional-grade software tools for modeling, analyzing, and visualizing complex networked systems. Built on high-performance multi-core CPU and GPU infrastructure, and equipped with a comprehensive suite of graph analytics libraries and graph database technologies, this testbed enables researchers and industry partners to study the structure, dynamics, and emergent properties of large-scale networks. Application domains include social network analysis, cybersecurity threat detection, biological network modeling, transportation systems, financial fraud detection, and knowledge graph construction. Mathematical graph theory and network science form foundational pillars of modern Artificial Intelligence, underpinning recommendation systems, graph neural networks (GNNs), and relational reasoning pipelines. By bridging classical network analysis with cutting-edge AI workflows, GRAPHNET supports the full analytics lifecycle from data ingestion and graph construction through to community detection, pathfinding, centrality analysis, and machine learning on graphs. This testbed contributes to the CITADELS Framework by providing accessible state-of-the-art graph analytics capabilities relevant to Industry 5.0 challenges such as resilient supply chain mapping, human-centric organizational network analysis, and data-driven infrastructure planning.