Most previous actor-node ranking algorithms for event-based social networks only consider how many events an actor participates in. However in event-based social networks, we should also consider the influence of events when we rank actor-nodes. In this paper we formally define event-based social networks and related concepts, then we propose rules to construct an event-based social network. Algorithms are presented to discover the activity and importance of each actor-node. We test the algorithms by analysing the DBLP data set. In the experiment actors in DBLP data set are ranked based on their activity, importance, and combination of activity and importance, respectively.