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Information cascades are ubiquitous in both physical society and onlinesocial 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 arelargely unknown. Here we explore a large-scale dataset including $432$ millioninformation cascades with explicit records of spreading traces, spreadingbehaviors, information content as well as user profiles. We find that thestructural complexity of information cascades is far beyond the previousconjectures. We first propose a ten-dimensional metric to quantify thestructural characteristics of information cascades, reflecting cascade size,silhouette, direction and activity aspects. We find that bimodal law governsmajority of the metrics, information flows in cascades have four directions,and the self-loop number and average activity of cascades follows power law. Wethen analyze the high-order structural patterns of information cascades.Finally, we evaluate to what extent the structural features of informationcascades can explain its dynamic patterns and semantics, and finally uncoversome notable implications of structural patterns in information cascades. Ourdiscoveries also provide a foundation for the microscopic mechanisms forinformation spreading, potentially leading to implications for cascadeprediction and outlier detection.

Structural patterns of information cascades and their implications for dynamics and semantics