Accurate analysis of complex chemical reaction networks is necessary for reliable prediction of reaction mechanism. Though quantum chemical methods provide a desirable accuracy, large computational costs are unavoidable as considering numerous reaction pathways on the networks. We proposed a graph-theoretic approach combined with chemical heuristics (named ACE-Reaction) in previous work [Chem. Sci. 2018, 9, 825], which automatically and rapidly finds out the most essential part of reaction networks just from reactants and products, and here we extended it by incorporating a stochastic approach for microkinetic modeling. To show its performance and broad applicability, we applied it to 26 organic reactions, which include 16 common functional groups. As a result, we could demonstrate that ACE-Reaction successfully found the accepted mechanism of all reactions, most within a few hours on a single workstation, and additional microkinetic modeling automatically discovered new competitive paths as well as a major path.