  Based on network analysis of hierarchical structural relations among Chinese
characters, we develop an efficient learning strategy of Chinese characters. We
regard a more efficient learning method if one learns the same number of useful
Chinese characters in less effort or time. We construct a node-weighted network
of Chinese characters, where character usage frequencies are used as node
weights. Using this hierarchical node-weighted network, we propose a new
learning method, the distributed node weight (DNW) strategy, which is based on
a new measure of nodes' importance that takes into account both the weight of
the nodes and the hierarchical structure of the network. Chinese character
learning strategies, particularly their learning order, are analyzed as
dynamical processes over the network. We compare the efficiency of three
theoretical learning methods and two commonly used methods from mainstream
Chinese textbooks, one for Chinese elementary school students and the other for
students learning Chinese as a second language. We find that the DNW method
significantly outperforms the others, implying that the efficiency of current
learning methods of major textbooks can be greatly improved.
