<p>Ionic conductivity underpins the operation of technologies ranging from fuel cells to solid-state batteries. A central challenge is to visualize and interpret the pathways that enable ion transport. Several complementary strategies have been developed: (1) geometric analyses of binding sites and channels, (2) conduction graph analysis, and (3) statistical distributions from trajectory generating methods. These approaches are deeply interconnected: geometric features define the vertices and edges of conduction graphs, while dynamic simulations provide probability distributions that enrich static connectivity maps. This article surveys these visualization strategies with an emphasis on graph-theoretical methods, highlighting how centrality measures from network theory and pathfinding algorithms can reveal critical conduction routes that include both static and time-dependent features. To bridge theory and practice, we provide a tutorial with scripts that implement three representative methods: flowthrough centrality, path analysis, and kinetic Monte Carlo, on a previously studied model system. By combining conceptual overview with practical tools, we aim to equip researchers with accessible techniques for mapping and interpreting ion conduction pathways across diverse materials.</p> Graphical abstract <p></p>