The number of publicly available Web services (WS) is continuously growing. To perform efficient WS discovery, it is desirable to organize the WS space. Works in this direction propose to group WS according to certain shared properties. Such groups commonly called communities are based either on similarity or on interaction between WS. In this paper we focus on the former, and propose a new network-based approach to extract communities from a WS collection. This process is three-stepped: first we define several similarity functions able to compare WS operations, second we use them to build so-called similarity networks, and third we identify communities under the form of specific structures in these networks. We apply our method on a collection of real-world WS and comment the resulting communities. Finally, we additionally provide an analysis and an interpretation of our similarity networks with a complex networks perspective.