Abstract. We study diffusion processes on mixed graphs generated by the Hermitian magnetic Laplacian and show that they induce a natural geometric representation of the network. The associated magnetic diffusion defines a map of the vertices into a complex Euclidean space, where distances reflect imbalances in the diffusion flow and encode both connectivity and directionality. We prove that this construction gives rise to a well-defined metric structure and characterize its geometric properties. In particular, we show that the induced geometry admits both Euclidean and hyperbolic interpretations through a suitable normalization, thereby linking diffusion dynamics with non-Euclidean geometry in a direct and constructive way. We further introduce angle-based quantities associated with the magnetic diffusion and relate them to the underlying complex structure of the embedding, as well as to projective geometry. The results provide a unified perspective in which geometry emerges intrinsically from diffusion on directed networks, offering a dynamical alternative to embedding methods based on prescribed ambient spaces.