  A growing set of applications consider the process of network formation by
using subgraphs as a tool for generating the network topology. One of the
pressing research challenges is thus to be able to use these subgraphs to
understand the network topology of information cascades which ultimately paves
the way to theorize about how information spreads over time. In this paper, we
make the first attempt at using network motifs to understand whether or not
they can be used as generative elements for the diffusion network organization
during different phases of the cascade lifecycle. In doing so, we propose a
motif percolation-based algorithm that uses network motifs to measure the
extent to which they can represent the temporal cascade network organization.
We compare two phases of the cascade lifecycle from the perspective of
diffusion-- the phase of steep growth and the phase of inhibition prior to its
saturation. Our experiments on a set of cascades from the Weibo platform and
with 5-node motifs demonstrate that there are only a few specific motif
patterns with triads that are able to characterize the spreading process and
hence the network organization during the inhibition region better than during
the phase of high growth. In contrast, we do not find compelling results for
the phase of steep growth.
