  Network motifs are patterns of over-represented node interactions in a
network which have been previously used as building blocks to understand
various aspects of the social networks. In this paper, we use motif patterns to
characterize the information diffusion process in social networks. We study the
lifecycle of information cascades to understand what leads to saturation of
growth in terms of cascade reshares, thereby resulting in expiration, an event
we call ``diffusion inhibition''. In an attempt to understand what causes
inhibition, we use motifs to dissect the network obtained from information
cascades coupled with traces of historical diffusion or social network links.
Our main results follow from experiments on a dataset of cascades from the
Weibo platform and the Flixster movie ratings. We observe the temporal counts
of 5-node undirected motifs from the cascade temporal networks leading to the
inhibition stage. Empirical evidences from the analysis lead us to conclude the
following about stages preceding inhibition: (1) individuals tend to adopt
information more from users they have known in the past through social networks
or previous interactions thereby creating patterns containing triads more
frequently than acyclic patterns with linear chains and (2) users need multiple
exposures or rounds of social reinforcement for them to adopt an information
and as a result information starts spreading slowly thereby leading to the
death of the cascade. Following these observations, we use motif based features
to predict the edge cardinality of the network exhibited at the time of
inhibition. We test features of motif patterns by using regression models for
both individual patterns and their combination and we find that motifs as
features are better predictors of the future network organization than
individual node centralities.
