  Dynamical processes, such as the diffusion of knowledge, opinions, pathogens,
"fake news", innovation, and others, are highly dependent on the structure of
the social network on which they occur. However, questions on why most social
networks present some particular structural features, namely high levels of
transitivity and degree assortativity, when compared to other types of networks
remain open. First, we argue that every one-mode network can be regarded as a
projection of a bipartite network, and show that this is the case using two
simple examples solved with the generating functions formalism. Second, using
synthetic and empirical data, we reveal how the combination of the degree
distribution of both sets of nodes of the bipartite network --- together with
the presence of cycles of length four and six --- explains the observed levels
of transitivity and degree assortativity in the one-mode projected network.
Bipartite networks with top node degrees that display a more right-skewed
distribution than the bottom nodes result in highly transitive and degree
assortative projections, especially if a large number of small cycles are
present in the bipartite structure.
