Today's GenAI can create images from text but can it generate complex networks? Take transportation as an example: "When the Knicks won the championship, what would traffic across NYC look like?" The existing approaches fall short of modeling both structured attributes and unstructured semantics of events, and capturing topological structures in simulating network event evolution. In this session we will present Net-Ev^2 (Network Event Evolution), a novel generative simulator that jointly leverages event cues while preserving network topology in simulations. Specifically, the framework consists of two stages, namely structure-guided masked pre-training and topology-aware diffusion process, which is achieved by U-Net-like graph downsampling and upsampling during denoising. At inference time, Net-Ev^2 can generate simulations using natural-language event input only, with greater flexibility for practical usage. Furthermore, we introduce Net-Ev^2-6.5M, a multimodal benchmark of aligned event and network traffic data across four large-scale road networks, as well as a new topology-aware metric, namely JL-MMD, to evaluate topological fidelity in generated network dynamics.