Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Information diffusion through blogspace
Proceedings of the 13th international conference on World Wide Web
Analysis of social voting patterns on digg
Proceedings of the first workshop on Online social networks
Analyzing patterns of user content generation in online social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Meme-tracking and the dynamics of the news cycle
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Social influence and the diffusion of user-created content
Proceedings of the 10th ACM conference on Electronic commerce
Power-Law Distributions in Empirical Data
SIAM Review
Centrality metric for dynamic networks
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Patterns of influence in a recommendation network
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Inferring Networks of Diffusion and Influence
ACM Transactions on Knowledge Discovery from Data (TKDD)
Building a role search engine for social media
Proceedings of the 21st international conference companion on World Wide Web
Spatio-temporal and events based analysis of topic popularity in twitter
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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How does information flow in online social networks? How does the structure and size of the information cascade evolve in time? How can we efficiently mine the information contained in cascade dynamics? We approach these questions empirically and present an efficient and scalable mathematical framework for quantitative analysis of cascades on networks. We define a cascade generating function that captures the details of the microscopic dynamics of the cascades. We show that this function can also be used to compute the macroscopic properties of cascades, such as their size, spread, diameter, number of paths, and average path length. We present an algorithm to efficiently compute cascade generating function and demonstrate that while significantly compressing information within a cascade, it nevertheless allows us to accurately reconstruct its structure. We use this framework to study information dynamics on the social network of Digg. Digg allows users to post and vote on stories, and easily see the stories that friends have voted on. As a story spreads on Digg through voting, it generates cascades. We extract cascades of more than 3,500 Digg stories and calculate their macroscopic and microscopic properties. We identify several trends in cascade dynamics: spreading via chaining, branching and community. We discuss how these affect the spread of the story through the Digg social network. Our computational framework is general and offers a practical solution to quantitative analysis of the microscopic structure of even very large cascades.