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A transition network (TN) is a graph-theoretical concept describing the transitions between (meta)stable states of dynamical systems. Here, we review methods to generate and analyze a TN for molecular systems. The appropriate identification of states and transitions from the potential energy surface of the molecule is discussed. We describe a formalism transforming a TN on a static energy surface into a time-dependent dynamic TN that yields the population probabilities for each system state and the interstate transition rates. Three analysis methods that help in understanding the dynamics of the molecular system based on the TN are discussed: (1) Disconnectivity graphs allow important features of the energy surface captured in a static TN to be visualized, (2) graph-theoretical methods enable the computation of the best transition paths between two predefined states of the TN, and (3) statistical methods from complex network analysis identify important features of the TN topology. A broad review of the literature is given, and some open research directions are discussed. [ABSTRACT FROM AUTHOR]

In this paper hierarchical clustering and self-organizing maps (SOM) clustering are compared by using molecular data of large size sets. The hierarchical clustering can represent a multi-level hierarchy which show the tree relation of cluster distance. SOM can adapt the winner node and its neighborhood nodes, it can learn topology and represent roughly equal distributive regions of the input space, and similar inputs are mapped to neighboring neurons. By calculating distances between neighboring units and Davies-Boulding clustering index, the cluster boundaries of SOM are decided by the best Davies-Boulding clustering index. The experimental results show the effectiveness of clustering for molecular data, between-cluster distance of low energy samples from transition networks is far bigger than that of "local sampling" samples, the former has a better cluster result, "local sampling" data nevertheless exhibit some clusters. [ABSTRACT FROM AUTHOR]

In this paper competitive learning cluster are used for molecular data of large size sets. The competitive learning network can cluster the input data, it only adapts to the node of winner, the winning node is more likely to win the competition again when a similar input is presented, thus similar inputs are clustered into same a class and dissimilar inputs are clustered into different classes. The experimental results show that the competitive learning network has a good clustering reproducible, indicates the effectiveness of clusters for molecular data, the conscience learning algorithm can effectively cancel the dead nodes when the output nodes increasing, the kinds of network indicates the effectiveness of clusters for molecular data of large size sets. [ABSTRACT FROM AUTHOR]