The escalating complexity of global digital infrastructures, characterized by the convergence of 5G, massive IoT deployments, and hyperscale cloud-to-edge continuums, has rendered traditional linear network management models obsolete. At the heart of this complexity lies the network topology—the intricate map of nodes and interconnections that dictates the flow, latency, and resilience of data. This review article explores the paradigm shift toward Graph Analytics for Network Topology Optimization. Unlike traditional tabular data analysis, graph analytics treats the network as a native mathematical graph, where routers, switches, and endpoints are vertices, and the communication links are edges. This relational perspective allows for the discovery of structural properties—such as centrality, community clusters, and bottleneck bottlenecks—that are invisible to classical monitoring. We categorize the core methodologies of graph-driven optimization, including the use of Graph Neural Networks (GNNs) for predictive traffic steering and PageRank-inspired algorithms for identifying critical infrastructure vulnerabilities. The article examines how graph analytics enables \"Topological Resilience,\" allowing networks to autonomously reconfigure their structure in response to failures or shifting demand. Furthermore, the review addresses the critical challenges of processing massive-scale dynamic graphs in real-time, the computational overhead of graph embeddings, and the necessity for explainable graph models in network operations. By synthesizing recent breakthroughs in spectral graph theory and combinatorial optimization, this paper provides a strategic roadmap for building \"Self-Optimizing Topologies.\" The findings suggest that graph analytics is the foundational intelligence required to manage the \"Relational Complexity\" of the 6G era, ensuring that global networks are not just faster, but fundamentally more robust, efficient, and adaptive.