  We consider entanglement-based quantum networks, where multipartite entangled
resource states are distributed and stored among the nodes and locally
manipulated upon request to establish the desired target configuration. The
separation of the generation process from the requests enables a
pre-preparation of resources, and hence a reduced network latency. It also
allows for an optimization of the entanglement topology, which is independent
of the underlying network geometry. We concentrate on establishing Bell pairs
or tripartite GHZ states between arbitrary parties. We study the influence of
noise in this process, where we consider imperfections in state preparation,
memories and measurements - all of which can be modeled by local depolarizing
noise. We compare different resource states corresponding to linear chains,
trees or multi-dimensional rectangular clusters, as well as centralized
topologies using bipartite or tripartite entangled states. We compute the
fidelity of the target states using a recently established efficient method,
the noisy stabilizer formalism and identify the best resource states within
these classes. This allows us to treat networks of large size containing
millions of nodes. We find that in large networks, high-dimensional cluster
states are favorable and lead to a significantly higher target state fidelity.
