Title
Structural patterns of information cascades and their implications for dynamics and semantics.
Abstract
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.
Year
Venue
Field
2017
arXiv: Social and Information Networks
Data mining,Anomaly detection,Social media,Structural complexity,Computer science,Silhouette,Information cascade,Theoretical computer science,Cascade,Semantics
DocType
Volume
Citations 
Journal
abs/1708.02377
2
PageRank 
References 
Authors
0.38
0
5
Name
Order
Citations
PageRank
Chengxi Zang1487.60
Peng Cui22317110.00
Chaoming Song358023.58
Christos Faloutsos4279724490.38
Wenwu Zhu54399300.42