  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.
