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

Understanding Information Flow in Cascades Using Network Motifs