  We present a reduction of Milestoning (ReM) algorithm to analyze the
high-dimensional Milestoning kinetic network. The algorithm reduces the
Milestoning network to low dimensions but preserves essential kinetic
information, such as local residence time, exit time, and mean first passage
time between any two states. This is achieved in three steps. First, nodes
(milestones) in the high-dimensional Milestoning network are grouped into
clusters based on the metastability identified by an auxiliary continuous-time
Markov chain. Our clustering method is applicable not only to time-reversible
networks but also to non-reversible networks generated from practical
simulations with statistical fluctuations. Second, a reduced network is
established via network transformation, containing only the core sets of
clusters as nodes. Finally, transition pathways are analyzed in the reduced
network based on the transition path theory. The algorithm is illustrated using
a toy model and a solvated alanine dipeptide in two and four dihedral angles.
