  Information cascades are ubiquitous in both physical society and online
social media, taking on large variations in structures, dynamics and semantics.
Although the dynamics and semantics of information cascades have been studied,
the structural patterns and their correlations with dynamics and semantics are
largely unknown. Here we explore a large-scale dataset including $432$ million
information cascades with explicit records of spreading traces, spreading
behaviors, information content as well as user profiles. We find that the
structural complexity of information cascades is far beyond the previous
conjectures. We first propose a ten-dimensional metric to quantify the
structural characteristics of information cascades, reflecting cascade size,
silhouette, direction and activity aspects. We find that bimodal law governs
majority of the metrics, information flows in cascades have four directions,
and the self-loop number and average activity of cascades follows power law. We
then analyze the high-order structural patterns of information cascades.
Finally, we evaluate to what extent the structural features of information
cascades can explain its dynamic patterns and semantics, and finally uncover
some notable implications of structural patterns in information cascades. Our
discoveries also provide a foundation for the microscopic mechanisms for
information spreading, potentially leading to implications for cascade
prediction and outlier detection.
