Given an evolving network and a set of user-specified keywords, how to discover and maintain the active events specified by the keywords? In this paper, we study the problem of event pattern discovery by keywords in graph streams. (1) We propose a class of event patterns to capture events relevant to user-specified keywords, by integrating (approximate) topological and value bindings from keywords. We also introduce an activeness measure, to balance the pattern expressiveness and the cost of pattern discovery. (2) We develop both from-scratch and incremental algorithms to discover and maintain active events in graph streams. Using real-world graph streams, we experimentally verify the effectiveness of the event pattern model and the efficiency of our from-scratch and incremental algorithms.