Keyphrases play a key role in text indexing, summarization and categorization. However, most of the existing keyphrase extraction approaches require human-labeled training sets. In this paper, we propose an automatic keyphrase extraction algorithm, which can be used in both supervised and unsupervised tasks. This algorithm treats each document as a semantic network. Structural dynamics of the network are used to extract keyphrases (key nodes) unsupervised. Experiments demonstrate the proposed algorithm averagely improves 50% in effectiveness and 30% in efficiency in unsupervised tasks and performs comparatively with supervised extractors. Moreover, by applying this algorithm to supervised tasks, we develop a classifier with an overall accuracy up to 80%.