  This paper is to analyze the properties of evolving bipartite networks from
four aspects, the growth of networks, the degree distribution, the popularity
of objects and the diversity of user behaviours, leading a deep understanding
on the empirical data. By empirical studies of data from the online bookstore
Amazon and a question and answer site Stack Overflow, which are both rating
bipartite networks, we could reveal the rules for the evolution of bipartite
networks. These rules have significant meanings in practice for maintaining the
operation of real systems and preparing for their future development. We find
that the degree distribution of users follows a power law with an exponential
cutoff. Also, according to the evolution of popularity for objects, we find
that the large-degree objects tend to receive more new ratings than expected
depending on their current degrees while the small-degree objects receive less
ratings in terms of their degrees. Moreover, the user behaviours show such a
trend that the larger degree the users have, the stronger purposes are with
their behaviours except the initial periods when users choose a diversity of
products to learn about what they want. Finally, we conclude with a discussion
on how the bipartite network evolves, which provides guideline for meeting
challenges brought by the growth of network.
