Chemical kinetics determines molecular stability, reaction selectivity and the evolution of reacting mixtures, but discovering the underlying reaction networks remains challenging. Here we introduce ReactNexus, a generative framework for kinetic-guided network exploration starting solely from reactant graphs. Generative models propose transition states and predict the full reaction trajectories connecting reactants and products, while calculations using a machine-learned interatomic potential verify connectivity and evaluate electronic barriers. On an in-distribution benchmark of 10,000 generated candidates, 80.8% connect to the input reactant after density functional theory (DFT) optimization and intrinsic reaction coordinate verification. The resulting networks uncover lower-barrier cooperative pathways and competition between catalyst release and catalyst-bound branches. In the examined carbocation cascades, coverage of known product precursors saturates after limited expansion and distinct entries converge onto shared intermediates, indicating a compact accessible reaction space. Network-derived candidate structures further assist mass-spectral interpretation of a complex nucleophilic aromatic substitution reaction. Together, these results establish a generative approach to reaction-network discovery and mechanistic analysis without predefined products or reaction templates.